23rd EuroQSAR

Fujita Award Lecture 2022 (AL04)
Consulting the Experiment: Are our Currently Applied Computational Drug-Design Tools Comprehensive Enough

Prof. Gerhard KLEBE

PHILIPPS-UNIVERSITY MARBURG, Marburg, Germany
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Prof. Gerhard KLEBE

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Gerhard Klebe studied chemistry at the University of Frankfurt/M, Germany, and obtained his doctorate in physical chemistry. After postdoctoral positions in crystallography (Grenoble, CNRS and ILL, France, Univ. of Berne, Switzerland) he was responsible for molecular modelling and crystallography at BASF-AG, Ludwigshafen, Germany. In 1996 he moved to Philipps-University of Marburg, Germany, as full professor in Pharmaceutical Chemistry. In 2005 he refused an offer from ETH Zürich, for a chair in Pharmaceutical Chemistry. Since 2020, he is retired. He served on editorial boards of several journals, was member of advisory boards of the CCDC, Cambridge, the of the Leibniz-Institute FMP in Berlin and Helmholtz-Institute in Saarbrücken. Research focus was directed toward the understanding of protein-ligand interactions, including chemical synthesis, microcalorimetry, molecular biology, crystallography, bioinformatics and software development. Internationally recognized software tools such as CoMSIA, AFMoC, DrugScore, Relibase/Cavbase or MOBILE have been developed in his laboratory. Several drug discovery projects concentrated on lead finding for neglected and poverty-related disease targets. To obtain better insight into affinity and selectivity determining features and to collect essential data for in-depth parametrization of software tools basic research has been performed on proteases, aldose reductase, kinases and hydrolases. He has written a text book on Drug Design which appeared in the German, English, and Chinese language. In 2011 the research of his group was awarded an ERC Advanced Grant on the “Chemogenomic profiling of drug-protein binding by shape, enthalpy/entropy and interaction kinetics” with particular emphasis on understanding the influence of water on ligand binding. In 2012 he received the Mannich-Medal by the German Pharmaceutical Society. Together with scientists at Bessy, HZB Berlin, a crystallography-based fragment screening beamline was established. In 2017, he was involved in the foundation of CrystalsFirst, a start-up in the fragment field. About 100 PhD students graduated from his laboratory. More than 400 scientific papers and over 1600 PDB entries emerged from his group. He organized biannually an International Workshop on New Approaches in Drug Discovery and Design and held the GRC on Computer Aided Drug Design in 2011.

Fujita Award Lecture 2020 (AL02)
Set-Theoretic Analysis of Ligand-Target Datasets - An Intuitionistic Fuzzy Set Approach
Prof. Vogt will stand in for Prof. Maggiora, awardee of the Fujita Award 2020.

Prof. Gerald M. MAGGIORA

UNIVERSITY OF ARIZONA, Tucson, United States
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Prof. Gerald M. MAGGIORA

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Gerald (‘Gerry’) Maggiora, PhD, received a Bachelor of Science in chemistry and a PhD in biophysics from the University of California, Davis. He has more than 20 years experience in academia as a professor of chemistry and biochemistry at the University of Kansas, as well as five years as a professor in the College of Pharmacy at the University of Arizona. He has a comparable amount of experience in the pharmaceutical industry, where he served as the Director of Computer-Aided Drug Discovery for three different companies. His early work spanned numerous fields related to the development of quantum mechanical and molecular mechanics methods and their application to problems of mechanistic organic chemistry, vision, photosynthetic energy conversion, and the structural chemistry of drugs, biomolecules, and proteins. After joining the pharma industry, he directed his efforts towards the development and application of similarity and diversity methods and the analysis of biologically relevant chemical space to drug research. Maggiora’s early university research interests were focused on computational studies of molecular systems in biology and chemistry including quantum chemistry, organic reaction mechanisms, photosynthesis, vision, potential energy functions, structural chemistry, structure-activity relationships and drug design. While in the pharmaceutical industry his research focused on computational aspects of drug design and chemical informatics with special emphasis on applications of chemical space, activity landscapes, and activity cliffs and their application to drug research. Currently, he is continuing his work on chemical spaces and on soft computing (e.g. fuzzy and rough set methods) approaches to SAR analysis applied to the identification and design of new bioactive molecules and drugs.

Dr Martin VOGT

UNIVERSITY OF BONN, Bonn, Germany
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Dr Martin VOGT

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Martin Vogt is a senior research scientist and lecturer in the Department of Life Science Informatics at the University of Bonn with a background in computer science and mathematics. He received his diploma (Master equivalent) in computer science in 2005 from the University of Bonn. From 2005 to 2008 he pursued and earned his Ph.D. working on virtual screening methods under the guidance of Prof. Jürgen Bajorath. He has continued to work in his group since then first as a post-doctoral fellow and later as a senior research scientist, completing his habilitation in 2015 in bioinformatics. His research interests include algorithmic method development in chemoinformatics, especially focusing on statistical methods for data mining and machine learning.

Hansch Award Lecture 2020 (AL01)
Multi-scaling the CRISPR-Cas Revolution from Gene Editing to Viral Detection

Dr Giulia PALERMO

UNIVERSITY OF CALIFORNIA RIVERSIDE, Riverside, United States
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Dr Giulia PALERMO

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Giulia Palermo is a computational biophysicist with expertise in molecular simulations. She is an Assistant Professor in the Department of Bioengineering at the University of California Riverside, and a cooperating Faculty in the Department of Chemistry. Her research uses computational biophysics to clarify the mechanism of action of biological systems of key importance for genome editing and regulation. She is a native of Italy where she earned her PhD in 2013 from the Italian Institute of Technology, working in the group of Dr. Marco De Vivo. She has been a post-doc in the group of Prof. Ursula Rothlisberger at the Swiss Federal Institute of Technology (EPFL), where she worked on ab-initio methods. In 2016, she has been awarded a Swiss National Science Foundation (NSF) post-doctoral fellowship to join the group of Prof. J. Andrew McCammon at the University of California San Diego, where she earned experience in novel multiscale methods enabling the study of increasingly realistic biological systems.

Hansch Award Lecture 2022 (AL03)
Data-Driven Methods for Active Compound Design and Risk Assessment

Prof. Andrea VOLKAMER

SAARLAND UNIVERSITY, Saarbrücken, Germany
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Prof. Andrea VOLKAMER

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https://volkamerlab.org/

Andrea Volkamer recently started a professorship at Saarland University in 'Data Driven Drug Design'. Before she wasan assistant professor in 'Structural Bioinformatics and /in silico/Toxicology' at the Institute of Physiology, Charité Universitätsmedizin Berlin. After earning her PhD from the University of Hamburg (2013), with focus on computational active site and druggability predictions, Andrea worked at BioMedX Innovation Center, Heidelberg, as a PostDoc researcher on tools to assist the development of selective kinase inhibitors in collaboration with Merck KGaA (2013-2016). Her main research focus is method development and application at the interface of structural bioinformatics and cheminformatics, with particular interest in structure-enabled machine learning approaches, applied in the context of computational drug design and /in silico/ toxicology.

Visualizing Data at Scale: Complex Science, Unruly Users, and the Vitruvian Triad (PL01)

Dr Dimitris AGRAFIOTIS

PFIZER, Philadelphia, United States
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Dr Dimitris AGRAFIOTIS

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Dimitris Agrafiotis, PhD, FRSC, is Vice President of Pfizer Digital, where he is responsible for leading the digital strategy and portfolio for Pfizer’s Worldwide Research, Development and Medical organization. Dr. Agrafiotis received his BS in chemistry from the University of Patras in 1985, and PhD in theoretical chemistry from Imperial College London in 1988, and held postdoctoral fellowships at the University of California, Berkeley and Harvard, where he worked with Nobel laureate EJ Corey. In 1991, he joined Parke-Davis as Senior Scientist in the computational drug design group, and in 1994 he moved to 3-Dimensional Pharmaceuticals as a founding member of its scientific staff, responsible for building the company’s informatics and computational drug design capabilities. Following a successful IPO and the acquisition of the company by Johnson & Johnson in 2003, he was appointed Senior Research Fellow and Team Leader of Molecular Design and Informatics, a position he held until 2006 when he was appointed Vice President and Global Head of Informatics and Research and Early Development IT. In 2013, he joined Covance as Chief Data Officer and Head of Technology Products where he developed Xcellerate, a multi-award-winning clinical platform and the first software-as-a-service offering in the company's history, used by many biopharmaceutical companies around the world to improve the design, execution and monitoring of their clinical trials. In 2019 he moved to Novartis as Chief Information Officer of the Novartis Institutes for Biomedical Research, where he was responsible for leading Novartis' global technology and informatics organization for its research and early development division, where he stayed until 2021 when he moved to Pfizer. His work is documented in more than 100 peer-reviewed publications and book chapters and 18 issued US patents. In 2012 he was elected Fellow of the Royal Society of Chemistry for his contributions to chemical and pharmaceutical research, and in 2016 he was named in Computerworld’s Premier 100 Technology Leaders for his technology leadership and innovative approaches to business challenges.

Mesoscale Simulations Reveal Unseen Vulnerabilities of Viral Glycoproteins (PL06)

Prof. Rommie AMARO

UNIVERSITY OF CALIFORNIA, San Diego, United States
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Prof. Rommie AMARO

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http://amarolab.ucsd.edu

Enhanced Sampling Atomistic Simulations for The Estimation of Drug Binding Kinetics (PL11)

Prof. Paolo CARLONI

FORSCHUNGSZENTRUM JÜLICH, Jülich, Germany
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Prof. Paolo CARLONI

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Paolo Carloni is Chair on Theoretical Biophysics at RWTH Aachen University, Germany, Director of the Computational Biomedicine section (INM-9/IAS-5) of the Institute for Neuroscience and Medicine (INM) and Institute for Advanced Simulation (IAS), and Co-Director of the JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Germany. He got his PhD at the University of Florence, Italy in 1994, under the supervision of Lucia Banci, Pier Luigi Orioli and Michele Parrinello (then at the IBM Research Laboratory in Zurich, Switzerland) on “Molecular simulations of metalloproteins”. His research focuses on development and application of HPC-based molecular simulation to problems of neurobiological interest (https://scholar.google.com/citations?user=G2 R_F1sAAAAJ&hl=en). He has published more than 310 papers and given more than 280 talks in conferences, universities and research institutes. He has supervised more than 55 PhD students. He is part of several EU projects, from the Human Brain Project to the Center for Computational Biology “BioExcel” and to the ITN program “Stimulate“.

Towards Machine Learning-Driven Drug Development (PL09)

Prof. Olivier ELEMENTO

CORNELL UNIVERSITY, New York, United States
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Prof. Olivier ELEMENTO

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Research Field
Precision Medicine, AI, Computational Biomedicine

Education
BS, Mechanical Engineering / University Paul Sabatier, Toulouse (FR)
MS, Mechanical Engineering / INSA Toulouse (FR) / University of Nottingham (UK)
MS, Intelligent Systems (Artificial Intelligence) / University of Paris Dauphine (FR)
PhD, Computational Biology / University of Montpellier (FR) / CNRS / LIRMM

Former professional experience
2009-2014 Assistant Professor, Dept of Physiology and Biophysics, Weill Cornell Medical College, NY
2009-2014 Assistant Professor, Institute for Computational Biomedicine, Weill Cornell Medical College, NY
2014-2019 Associate Professor, Dept of Physiology and Biophysics, Weill Cornell Medical College, NY

Efficient Computational Strategies for Increasingly Accurate Representations of Metastable Conformational States of G Protein-Coupled Receptors and their Kinetic Relations (PL04)

Prof. Marta FILIZOLA

ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI, New York, United States
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Prof. Marta FILIZOLA

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Dr Marta Filizola is the Sharon & Frederick A. Klingenstein-Nathan G. Kase, MD Professor in the Departments of Pharmacological Sciences and Neuroscience, as well as the Dean of The Graduate School of Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, in New York, USA. The overall goal of her research program is to obtain rigorous mechanistic insights into the structure, dynamics, and function of important classes of membrane proteins and prominent drug targets, including G protein-coupled receptors (GPCRs), transporters, channels, and B3 integrins. To this end, her lab uses several computational structural biology tools and rational drug design approaches, ranging from molecular modeling, bioinformatics, cheminformatics, molecular dynamics simulations, free-energy perturbations, machine learning, etc. A native of Italy, she received her Bachelor’s and Master’s degrees in Chemistry from the University Federico II in Naples. She pursued a PhD in Computational Chemistry at the Second University of Naples and a postdoctorate in Computational Biophysics at the Molecular Research Institute in California, USA.

Promiscuity of Ligand Binding: From Off-target Prediction to Fragment-based Design (PL08)

Prof. Oliver KOCH

UNIVERSITY OF MÜNSTER, Münster, Germany
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Prof. Oliver KOCH

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PD Dr Oliver Koch is currently an independent group leader for computational medicinal chemistry and molecular design at the University of Münster. He studied pharmacy and computer science at the University of Marburg, where he also received his PhD in pharmaceutical chemistry. After a postdoctoral research stay at the Cambridge Crystallographic Data Centre in 2008, he joined MSD Animal Health Innovation GmbH, before becoming a junior group leader at TU Dortmund University. Since January 2019, he is a member of the Institute of Pharmaceutical and Medicinal Chemistry, University of Münster, where he recently received the Venia Legendi for Pharmaceutical and Medicinal Chemistry.

His research interests lie in the development and application of computational methods in rational drug design with focus on structure-based design and ‘big data’ driven decisions combined with artificial intelligence in order to develop bioactive molecules and to understand selectivity, promiscuity and polypharmacology of protein-ligand interactions

Can Humans Learn from Machine Learning in Drug Discovery? (PL12)

Prof. Tudor I. OPREA

ROIVANT SCIENCES INC., Albuquerque, United States
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Prof. Tudor I. OPREA

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Tudor Oprea is currently Vice President, Translational Informatics at Roivant Sciences Inc, in Boston MA (USA). He is also Emeritus Professor of Medicine at the University of New Mexico Health Sciences Center in Albuquerque, New Mexico (USA). He served as Guest Professor at the Gothenburg University in Sweden and at the University of Copenhagen, Denmark between 2018 and 2022. Dr Oprea holds an MD (general medicine) and a PhD (molecular physiology) from the University of Medicine and Pharmacy, Timişoara, Romania. He previously held positions at the University of Utrecht, Washington University in St. Louis, Los Alamos National Laboratory and AstraZeneca Gothenburg.

Dr Oprea’s translational research led to clinical trials, most notably R-ketorolac as Cdc42/Rac1b inhibitor in ovarian cancer; and LNS8801, a selective GPER agonist for melanoma. He served as Principal Investigator for the “Illuminating the Druggable Genome” Knowledge Management Center, and has co-developed several databases including Pharos (pharos.nih.gov) and DrugCentral (drugcentral.org). He has co-authored over 300 publications, 10 granted US patents and co-edited 2 books on informatics in drug discovery.

His current research is in the development of validated artificial intelligence models for target selection in drug discovery, by combining numerical and free-text information to model human health via knowledge graphs.

Cheminformatics for Next Generation Make-on-Demand Compound Catalogs (PL02)

Prof. Matthias RAREY

UNIVERSITY OF HAMBOURG, Hamburg, Germany
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Prof. Matthias RAREY

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https://uhh.de/rarey

Matthias Rarey is Full Professor for Bioinformatics at the Universität Hamburg since 2002. Currently, he heads the Center for Bioinformatics in Hamburg, is co-spokesperson of the Helmholtz Data Science Graduate School DASHH and of the Center for Data and Computing in Natural Science (CDCS). Before he came to Hamburg, he worked at Fraunhofer SCAI with research stays at SmithKline Beecham (PA, USA) and Roche Bioscience (CA, USA). He is co-founder and shareholder of BioSolveIT GmbH located in Sankt Augustin, Germany. With his background in computer science (PhD and Habilitation from RFW Universität Bonn) Matthias’ research focus is on new computational approaches in Cheminformatics and Molecular Design. The whole bandwidth of modern computing from numerical and combinatorial optimization via database technologies to machine learning and visual analytics underlies his methods. Computational tools from his group like FlexX, FTrees-FS, Recore, Hyde are characterized by tailor-made algorithmic solutions paired with precise chemical models. Many of them are in practical use in pharmaceutical research worldwide. Matthias is currently associate editor of Journal of Chemical Information and Molecular Design (JCIM) and Vice-Chair of the Gordon Research Conference on Computer-Aided Drug Design.

Insights into the Passive Membrane Permeation Process of Cyclic Peptides (PL07)

Prof. Sereina RINIKER

ETH ZÜRICH, Zurich, Switzerland
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Prof. Sereina RINIKER

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http://www.riniker.ethz.ch

Sereina Riniker is currently Associate Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences at ETH Zurich. In June 2014, she came as Assistant Professor (with Tenure Track) to ETH Zurich, and was promoted in April 2020 to Associate Professor. Her research focusses on methodology development at the interface of classical molecular dynamics simulations and cheminformatics, and the study of challenging biological and chemical questions with computational tools. She holds a Master degree in chemistry and finished her PhD in computational chemistry at ETH Zurich in 2012. From 2012 to 2014, she worked as postdoctoral fellow at the Novartis Institutes for BioMedical Research in Basel and Cambridge, Massachusetts.

Enhancing Confidence in Computational Methods for the Evaluation of Drug Safety (PL05)

Dr Alessandra RONCAGLIONI

MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH, Milano, Italy
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Dr Alessandra RONCAGLIONI

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Dr Alessandra Roncaglioni is currently Head of the Research Unit on Computational Toxicology in the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Her research interests are related to the use of computational chemistry methods applied to toxicological and environmental health issues with particular emphasis on in silico approaches (such as QSAR and read-across) and their use in different regulatory framework (e.g. REACH, Plant Protection Products, drug impurities).

She is co-author of about 50 papers in peer reviewed journal and more then 30 communications in international conferences.

She graduated in Environmental Science from the Università degli Studi di Milano-Bicocca and got a PhD in Life and Biomolecular Science from the Open University, UK.

Combining Multi-Omics and Network Knowledge to Study Diseases and Therapies (PL03)

Prof. Julio SAEZ-RODRIGUEZ

HEIDELBERG UNIVERSITY, Heidelberg, Germany
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Prof. Julio SAEZ-RODRIGUEZ

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Julio Saez-Rodriguez is Professor of Medical Bioinformatics and Data Analysis at the Faculty of Medicine of Heidelberg University, director of the Institute for Computational Biomedicine, group leader of the EMBL-Heidelberg University Molecular Medicine Partnership Unit and a co-director of the DREAM challenges. He holds a PhD (2007) in Chemical Engineering. He was a postdoctoral fellow at Harvard Medical School and M.I.T (2007- 2010), group leader at EMBL-EBI, Cambridge (2010-2015), and professor of Computational Biomedicine at RWTH Aachen (2015-2018). His research focuses on computational methods to understand and treat the deregulation of cellular networks in disease (www.saezlab.org).

Exploring Alchemical Binding Free Energy Calculations in Drug Discovery (PL10)

Dr Gary TRESADERN

JANSSEN, Beerse, Belgium
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Dr Gary TRESADERN

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Gary Tresadern is a Scientific Director in the European CADD group at Janssen R&D and heads a team of 8 people. He joined Janssen in 2005 and has held various positions and responsibilities: head of the EU molecular informatics group, head of the global kinase platform, co-lead on neuroscience hit-generation group, responsible for external structural biology support, global lead on MD/FE activities. Throughout his time at Janssen he has worked as a molecular modeler in drug discovery programs for different therapeutic areas and contributed to over a half a dozen projects reaching clinical evaluation. He has a broad interest in hit and lead generation and at Janssen has been responsible for initiating and leading collaborations in areas beyond computational chemistry, such as fragment screening, protein NMR, novel screening approaches, and structural biology. In the early part of his career his scientific interests leaned towards virtual screening while during the last ten years he has developed expertise in MD and free energy calculations and helped explore and introduce the methods to drug discovery projects at Janssen. In the early 2000’s, prior to joining Janssen, he worked at Tripos in the UK and prior to that graduated with a PhD in computational chemistry from the University of Manchester focused on QM/MM and enzyme dynamics and kinetics. He is co-author of over 90 scientific articles and co-inventor on 25 patents.

Deep Docking - Deep Learning Based QSAR Approach for Augmenting Structure-Based Drug Discovery (SL01)

Prof. Artem CHERKASOV

UNIVERSITY OF BC, VANCOUVER PROSTATE CENTRE, Vancouver, Canada
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Prof. Artem CHERKASOV

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Artem Cherkasov is a Professor of Medicine at the University of British Columbia (Vancouver, Canada) and a Tier 1 Canada Research Chair in Precision Cancer Drug Design. Research interests include Computer-Aided Drug Discovery (CADD), Artificial Intelligence, QSAR, Cheminformatics, and development of personalized cancer therapies. Dr. Cherkasov received his PhD From Kazan University in Russia and completed postdoctoral training at the Royal Institute of Technology in Stockholm Sweden and University of Saskatchewan in Canada. Dr. Cherkasov co-authored more than 200 research papers, 80 patent filings and several prominent book chapters. During his tenure at the UBC, Dr. Cherkasov has been a principal applicant or co-applicant on a number of successful grants totalling over 80M dollars, and licenced 8 drug candidates to big pharma, major international venture funds and spinoff companies.

40 years of Molecular Interaction Fields (SL06)

Prof. Gabriele CRUCIANI

UNIVERSITY OF PERUGIA, Perugia, Italy
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Prof. Gabriele CRUCIANI

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Gabriele Cruciani is full professor of Organic Chemistry and Cheminformatics at the University of Perugia, Italy. He’s also a University deputy for technology transfer. Moreover, is the scientific director of Molecular Discovery company based in London, of Molecular Horizon based in Perugia, Italy, and of Montelino Therapeutics based in Boston, USA. He’s also the director of the human Cytochrome Consortium Initiative (a consortium of seven pharmaceutical companies collaborating to address metabolism issues in predictive human metabolism) and a scientific advisory board of several pharmaceutical companies. Prof. Cruciani made significant contributions to the field of ADME and computer-aided drug design, due to his over two decades of software production for data mining and drug discovery experience, focused on drug informatics, cheminformatics, small-molecule and target informatics, and virtual screening. He obtained several awards plus a number of support grants from pharmaceutical companies. Cruciani is included in the Top Italian Scientists list.

Structure-Activity Relationships from Drug-Receptor Complexes Using the Comparative Binding Energy (Combine) Method (SL05)

Prof. Federico GAGO

UNIVERSITY OF ALCALA, Alcala de Henares, Spain
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Prof. Federico GAGO

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Federico Gago studied Pharmacy at Complutense University, Madrid, and followed post-doctoral studies at the Physical Chemistry Laboratory, Oxford University, under the supervision of Prof. W. Graham Richards. He teaches Pharmacology at the Schools of Pharmacy and Medicine in the University of Alcalá, near Madrid, where he is Full Professor of Pharmacology in the Department of Biomedical Sciences. He was Associate Director of the NFCR Center for Computational Drug Design (Oxford) from 2001 to 2006 and a member of the Editorial Advisory Board of Journal of Medicinal Chemistry from 2006 to 2010. Since 2001 he has been serving as an Editor-in-Chief for Journal of Computer-Aided Molecular Design. Prof. Gago is a member of the Spanish Societies of Medicinal Chemistry, Biochemistry and Molecular Biology, Biophysics, and Pharmacology, as well as of the Spanish Association for Cancer Research and the American Chemical Society. He has authored over 200 research papers in specialized scientific journals and published several reviews and book chapters. His current research interests are in the areas of catalytic mechanisms in enzymes, receptor-based structure-activity relationships, structure-based drug design, and computer simulations of drug-targeted macromolecules including DNA, enzymes and pharmacological receptors.

Quantifying GPCR Signaling in an Oligomeric Context (SL07)

Dr Jesús GIRALDO

AUTONOMOUS UNIVERSITY OF BARCELONA, Bellaterra, Spain
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Dr Jesús GIRALDO

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Jesús Giraldo obtained his PhD in Chemistry at Universitat Autònoma de Barcelona (UAB) in 1992. He carried out postdoctoral stays in 1995 and 1996 at the Service de Conformation des Macromolécules Biologiques et de Bioinformatique (Université Libre de Bruxelles), headed by Prof. Shoshana J. Wodak. In 1997 Dr. Giraldo became associate professor of the Biostatistics Unit of the Faculty of Medicine (UAB). He joined the Neurosciences Institute (UAB) on its foundation in 2003, and established there his research group. Along the latter period his research focused mainly on the elucidation by computational methods of the mechanisms of G protein-coupled receptor signal transduction. Currently, Jesús Giraldo is the director of the Neuroscience Institute.

Neural Networks Learning Computational Chemistry (SL16)

Dr Olexandr ISAYEV

CARNEGIE MELLON UNIVERSITY, Pittsburgh, United States

The Challenges Associated with Building Accurate Predictive Cytochrome P450 Inhibition Models Using Machine Learning Approaches (SL13)

Dr Petrina KAMYA

INSILICO MEDICINE, Hong Kong, Hong Kong

Integrating Toxicity and Metabolism Prediction (SL02)

Prof. Johannes KIRCHMAIR

UNIVERSITY OF VIENNA, Vienna, Austria
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Prof. Johannes KIRCHMAIR

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http://comp3d.univie.ac.at

Johannes Kirchmair is an associate professor in cheminformatics at the Department of Pharmaceutical Sciences of the University of Vienna. His main research interests include the development and application of computational methods for the prediction of the biological activities, metabolic fate and toxicity of small organic molecules. After earning his PhD from the University of Innsbruck (2007), Johannes started his career as an application scientist at Inte:Ligand GmbH (Vienna). In 2010 he joined BASF SE (Ludwigshafen) as a postdoctoral research fellow. Thereafter he worked as a research associate at the University of Cambridge (2010-2013) and ETH Zurich (2013-2014). Johannes held a junior professorship in applied bioinformatics at the University of Hamburg (2014 to 2018) and an associate professorship in bioinformatics at the University of Bergen (2018 to 2019).

Assessing the Suitability of 3D QM-Derived Atomic Hydrophobicity Patterns for Ligand-Target Interactions (SL12)

Prof. F. Javier LUQUE

UNIVERSITY OF BARCELONA, Barcelona, Spain
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Prof. F. Javier LUQUE

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F. Javier Luque obtained his PhD in Chemistry at Universitat Autònoma de Barcelona in 1989. He carried out post-doctoral stays at the Swiss Federal Institute of Technology in Zurich (1992), University of Pisa (1994) and University of Nancy (1998). In 1991 he was appointed Full Professor at the University of Barcelona (UB) in 2003. He is also member leading the Computational Biology and Drug Design research team at the UB. He is also member of the Institute of Biomedicine (IBUB) and Institute of Theoretical and Computational Chemistry (IQTCUB), both pertaining to the UB. His research interest are focused on the study of biomolecular systems with the aim to explore the relationships between structure, dynamics and function, understand the molecular determinants involved in biomolecular recognition, and apply this knowledge in the design of new bioactive compounds.

Opportunities and Challenges in GPCR SBDD: Finding the Sweet Spots (SL08)

Dr Pierre MATRICON

SOSEI HEPTARES, Cambridge, United Kingdom
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Dr Pierre MATRICON

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Pierre Matricon obtained his PhD in 2021 at Uppsala university (Sweden). His work in Jens Carlsson’s lab involved the progression of GPCR antagonist fragment ligands guided by alchemical free energy simulations, the development of a structure-based virtual screening strategy to identify subtype-selective GPCR antagonists, and enhanced sampling MD simulations to study GPCR activation with scope for the design of GPCR ligands with tailored efficacy profiles. Mid 2021, Pierre Matricon joined the Computational Chemistry team of Sosei Heptares, an international biopharmaceutical group focused on the design and development of new medicines originating from its proprietary GPCR-targeted StaR® technology and SBDD platform capabilities (www.soseiheptares.com). In this team, led by Chris de Graaf, Pierre works on the development and application of CADD approaches across the GPCRome to help Sosei Heptares advance a broad and deep pipeline of partnered and in-house drug candidates in multiple therapeutic areas.

Translational Safety Meets Pharmacovigilance (SL10)

Prof. Jordi MESTRES

IMIM HOSPITAL DEL MAR MEDICAL RESEARCH INSTITUTE, Girona, Spain
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Prof. Jordi MESTRES

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Jordi Mestres holds a PhD in Computational Chemistry from the University of Girona. After a post-doctoral stay at Pharmacia&Upjohn in Kalamazoo (Michigan, USA), in 1997 he joined the Molecular Design & Informatics department at N.V. Organon in Oss (The Netherlands) and in 2000 he was appointed Head of Computational Medicinal Chemistry at Organon Laboratories in Newhouse (Scotland, UK). In 2003, he took on his current position as Head of the Research Group on Systems Pharmacology, within the Research Program on Biomedical Informatics at the IMIM Hospital del Mar Medical Research Institute in Barcelona. He is also Associate Professor at the University Pompeu Fabra (UPF). In 2006, he founded Chemotargets as a spin-off company of his group. He is also the recipient of the 2006 Corwin Hansch Award from the QSAR and Modelling Society and the 2007 Technology Transfer Award from the UPF. In 2018, he was admitted as a Fellow of the Royal Society of Chemistry. His expertise and research interests focus on the development of novel computational approaches and design of new analytics tools to predict and visualize the pharmacology and safety profiles of small molecule pharmaceuticals. He is the author of over 160 publications, 10 patents among them.

Machine Learning for Early Toxicity Detection at Bayer (SL09)

Dr Floriane MONTANARI

BAYER AG, Berlin, Germany
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Dr Floriane MONTANARI

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Floriane Montanari is a research scientist with years of experience in cheminformatics and machine learning. She received her PhD from the University of Vienna in 2016. Her thesis focused on liver ABC-transporter inhibition and best practices in model validation. She joined Bayer AG in 2017. She has a passion for practical and useful machine learning approaches applied at early stages of the drug discovery pipeline. One of her achievements has been to improve the daily work of medicinal chemists company-wide by productionizing her work on deep learning for ADMET properties. Recently, she has focused on aspects of model explainability as well as liver toxicity.

She is the co-author of more than twenty scientific publications and co-PI representing Bayer in the Horizon2020 project “OnTox” whose vision is to provide a framework for animal-free risk assessment of chemicals

Covalent Reversible Inhibition of Rhodesain; A Key Player in African Sleeping Sickness (SL14)

Prof. Tanja SCHIRMEISTER

UNIVERSITY OF MAINZ, Mainz, Germany

Chemography Concept in Chemical Space Analysis (SL03)

Prof. Alexandre VARNEK

UNIVERSITY OF STRASBOURG, Strasbourg, France
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Prof. Alexandre VARNEK

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Alexandre Varnek is a full professor in theoretical chemistry, head of the Laboratory of Chemoinformatics at the University of Strasbourg. He also is a head of the Chemoinformatics group at the Hokkaido University (Japan). He is an Editor-in Chief of the journal “Molecular Informatics (WILEY).

- Research field: Chemoinformatics

- Education:
PhD in physical chemistry, Russ. Ac. Sci.;
Habilitation in theoretical chemistry: University of Strasbourg

Contextualizing Ligand-Transporter Interactions with Data-Driven Approaches (SL11)

Dr Barbara ZDRAZIL

EMBL-EBI, Hinxton, United Kingdom
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Dr Barbara ZDRAZIL

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Barbara Zdrazil is currently employed as ChEMBL Coordinator at the European Bioinformatics Institute (EMBL-EBI) in Hinxton, Cambridge, UK. Prior to this role, Barbara was a Group Leader at the Department of Pharmaceutical Sciences, University of Vienna, Austria, and worked as a Safety Data Scientist for EMBL-EBI. She received her PhD in Pharmaceutical Chemistry from the University of Vienna in 2006 and performed her postdoctoral studies at the University of Düsseldorf, Germany. Barbara contributed to many EU-funded projects (eTOX, OpenPHACTS, EU-ToxRisk) and was leading a nationally funded FWF project focusing on hepatic SLC transporters from 2017-2021. In 2019, Barbara accomplished her Venia Docendi in Pharmacoinformatics at the University of Vienna. Barbara's research interests include large-scale data analyses including time trend analyses, data-driven molecular design approaches, and computational toxicology approaches. Since January 2022, Barbara is also Co-Editor in Chief of the Journal of Cheminformatics.

Disentangling Host and Microbiome Contributions to Drug Pharmacokinetics and Toxicity (SL04)

Dr Maria ZIMMERMANN

EMBL, Heidelberg, Germany
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Dr Maria ZIMMERMANN

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Maria is a group leader in Genome Biology Unit at EMBL Heidelberg since 2021, investigating microbial systems with a combination of computational and experimental approaches. Maria has interdisciplinary background in computer science and systems biology. Her PhD project at ETH Zurich included developing machine learning-based methods for metabolic flux analysis and multiomics integration approaches to investigate metabolic adaptation in pathogenic bacteria. For postdoctoral training, Maria moved to the human microbiome field to investigate the molecular mechanisms of host-microbiome interactions in the context of drug metabolism. Her research group combines computational modelling and multi-omics data integration to investigate metabolic interactions within microbial communities and between microbiota and the host in the context of medical drugs, xenobiotics and dietary components.

QRNN: Transferable Neural Network for Potential Energy Surfaces of Closed-Shell Organic Molecules Including Ions (OC09)

Dr Stephan EHRLICH

SCHRÖDINGER GMBH, Mannheim, Germany

Derivation of Molecular Substructures Enhancing Drug Activity in Gram-Negative Bacteria (OC05)

Mr Dominik GURVIC

UNIVERSITY OF DUNDEE, Dundee, United Kingdom
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Mr Dominik GURVIC

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Dom is Lithuanian and has completed his Physics Undergraduate degree with Honors at the University of Dundee in 2019. He then started a PhD in the Computation Biology Division of LifeSciences in the same year. In his PhD, he studies antibacterial resistance using self-developed chemoinformatic and machine learning toolkits.

In Silico Design of Tubulin Activity Modulators (OC08)

Dr Dragos HORVATH

CNRS , Strasbourg, France
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Dr Dragos HORVATH

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Dragos Horvath graduated in 1991 as a chemical engineer at the University of Cluj-Napoca, Romania, where he continued as teaching assistant until departure (1992-1996) for MSc and PhD studies in Lille (Institut Pasteur, Prof. Andrè Tartar) and Brussels (Université Libre, Prof. Shoshana Wodak). As co-founder of the combinatorial chemistry team of Cerep, he acted as Head of Molecular Modeling (worldwide) for this company until 2003, when he returned to academic research as a CNRS Scientist (2003) and then Research Director (2011). Since 2007 he joined Alexandre Varnek’s Chemoinformatics Laboratory at the University of Strasbourg. He is active in both methodological development and applications of 3D modeling (conformational sampling, docking) and 2D chemoinformatics (molecular descriptors, QSAR, chemical space mapping, chemical reactivity, etc.)

Multi-Target QSAR Modeling for the Identification of Novel Inhibitors Against Alzheimer's Disease (OC02)

Mr Vinay KUMAR

JADAVPUR UNIVERSITY, Kolkata, India
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Mr Vinay KUMAR

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Vinay Kumar is a Ph.D. research scholar in the Department of Pharmaceutical Technology at Jadavpur University, Kolkata, India in Prof. Kunal Roy's research group since March 2018. He has completed his B. Pharm. (2015) from Dr. APJ Abdul Kalam Technical University Lucknow, India, and M.S. in Pharmacy (2017) degree from the National Institute of Pharmaceutical Education and Research (NIPER), Kolkata, India. He has awarded with Fellowship as Senior Research Fellow (SRF) by the Department of Atomic Energy-Board of Research in Nuclear Sciences (DAE-BRNS) and the Indian Council of Medical Research (ICMR). He has experience in QSAR, Chemometric modeling studies, Molecular Modeling, and Computer-Aided Drug Design with special reference to the development of inhibitors for the treatment of Alzheimer's disease. He has published 18 research and review articles, and 2 book chapters to date. At present time, actively working on the "Molecular Modelling of Potential anti-Alzheimer Agents Using Chemoinformatics Tools" as a registered Ph.D. scholar in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

Free Energy Predictions Using Deep Learning in Combination with Targeted Free Energy Perturbations (OC03)

Ms Soo Jung LEE

UNIVERSITY OF BASEL, Basel, Switzerland
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Ms Soo Jung LEE

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Soo Jung is a PhD student at the University of Basel, Switzerland, in the Computational Pharmacy Group supervised by Dr. Markus Lill. She has received her Bachelor and Master from Yonsei Uni-versity, South Korea, in the department of Life Science and Biotechnology. Her current research focus is on the application of Deep Learning in drug discovery.

The Use of Deep Neural Networks on Molecular Dynamics Simulations for the Prediction of Binding Affinities (OC06)

Mr Pierre-Yves LIBOUBAN

INSTITUTE OF ORGANIC AND ANALYTICAL CHEMISTRY, Orléans, France
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Mr Pierre-Yves LIBOUBAN

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After obtaining a master’s degree at the University of Rennes in the field of Pharmacy (2016-2019) and a master degree at the University of Paris Diderot In the field of in-silico drug design (2018-2019), I attended an internship in Bioinformatics at Institut Pasteur in Paris for 6 months. In 2019, I started my PhD studies at the Institute of Organic and Analytical Chemistry (ICOA) under the supervision of the professor Pascal Bonnet in the Structural Bioinformatics and Chemoinformatics group. In collaboration with the drug discovery company Janssen, my PhD project focuses on improving binding affinity prediction using recent deep learning algorithms combined to molecular dynamics simulations.

Multiscale Molecular Dynamics: An Efficient Tool for Assessing the Affinity and Specificity of Covalent Inhibitors (OC10)

Dr Levente Márk MIHALOVITS

RESEARCH CENTRE FOR NATURAL SCIENCES, Budapest, Hungary
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Dr Levente Márk MIHALOVITS

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Levente Márk Mihalovits studied chemical engineering at the Budapest University of Technology and Economics. He finished the pharmaceutical engineer MSc in 2018. Thereafter, he started his PhD studies under the guidance of Prof. György G. Ferenczy at the Medicinal Chemistry Research Group (Research Centre of Natural Sciences, Budapest) led by Prof. György Miklós Keserű as a student of the György Oláh Doctoral School. During the four years of PhD, his main focus was the computational characterization of covalent inhibition using a wide scope of computational chemistry methods including quantum chemical and hybrid QM/MM calculations. He received his PhD in 2022.

What Defines the Length of Drug-Target Residence Time of a Small-Molecule Inhibitor: Insights from Molecular Dynamics Simulations (OC04)

Dr Tatu PANTSAR

UNIVERSITY OF EASTERN FINLAND, Kuopio, Finland
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Dr Tatu PANTSAR

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Tatu Pantsar is a Senior Researcher at the School of Pharmacy of University of Eastern Finland. He is a pharmacist by training, and obtained his PhD (Pharm.) in the group of Prof. Antti Poso at the University of Eastern Finland in 2018. Tatu is specialised in computer-aided drug design and biomolecular simulations. He worked as a postdoc at the University Hospital Tübingen in 2018–2019 and as a Marie-Skłowdoloska Curie Research Fellow in the group of Prof. Stefan Laufer at the University of Tübingen in 2019–2021. His main interest is in cancer drug discovery. Currently, Tatu’s research focuses mainly on protein kinases and small GTPase Ras proteins.

3D Pride Without 2D Prejudice: Bias-Controlled Multi-Level Generative Models for Structure-Based Ligand Design (OC01)

Dr Carl POELKING

ASTEX PHARMACEUTICALS, Cambridge, United Kingdom

Fragment-Based and Pocket-Focused Library Design by Protein-Applied Computer Vision and Deep Generative Linking (OC07)

Dr Didier ROGNAN

CNRS, Illkirch, France
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Dr Didier ROGNAN

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Didier Rognan heads the Laboratory of Therapeutic Innovation (LIT) at the Faculty of Pharmacy of Strasbourg (France). He studied Pharmacy at the University of Rennes (France) and did a Ph.D. in Medicinal Chemistry in Strasbourg (France) under the supervision of Prof. C.G. Wermuth. After a post-doctoral fellow at the University of Tübingen (Germany, supervisor PD. Dr. G. Folkers), he moved as an Assistant Professor at the Swiss Federal Institute of Technology (ETH, Supervisor: Prof. G. Folkers) until October 2000. He was then appointed Research Director at the CNRS to build a new laboratory in Strasbourg. Dr. Rognan is scientific consultant for several pharmaceutical companies (e.g. Lilly, Sanofi, Servier, Solvay) and has created two start-up companies (IDEALP Pharma, medchem CRO) and BIODOL Therapeutics (innovative treatment of chronic pain). He is author of 190 publications and is mainly interested in all aspects of structure-based design and synthesis of bioactive compounds.

Privacy-Preserving Federated Learning at Unprecedented Scale Boosts Predictive Performance of Structure-Activity Modelling in Drug Discovery (OC11)

Dr Noé STURM

NOVARTIS, Huningue, France
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Dr Noé STURM

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Noé Sturm obtained a PhD in cheminformatics at the University of Strasbourg, France embedded at the Griffith Institute for Drug Discovery, Australia where he studied structural relationships between natural product biosynthetic enzymes and drug target proteins by binding site similarity. Since 2017, his main research interests focus on machine learning approaches accelerating small molecule hit discovery. At AstraZeneca, Noé contributed to the ExCAPE project investigating the potential of multi-task machine learning approaches on industrial scale structure activity datasets. Most recently Noé was heavily invested in the MELLODDY project as a Novartis contributor pioneering structure activity modelling at the scale of ten pharmaceutical companies.

Dr Wouter HEYNDRICKX

JANSSEN PHARMACEUTICALS, Beerse, Belgium
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Dr Wouter HEYNDRICKX

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Wouter Heyndrickx obtained his PhD in computational chemistry from the University of Bergen (UiB, Norway) in 2011. He spent up to 2019 in consulting, advising on and delivering solutions in the field of business intelligence, business analytics and machine learning for numerous clients, mainly in the financial and pharmaceutical industry. Thereafter, he joined Janssen Pharmaceutica, where he is currently part of a machine learning group providing support to research teams in early phases of small-molecule drug discovery.

Dr Tobias MORAWIETZ

BAYER AG, Wuppertal, Germany
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Dr Tobias MORAWIETZ

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Tobias Morawietz is a research scientist with extensive experience at combining machine learning and computational chemistry to accelerate in silico predictions for drug discovery applications. He studied biochemistry and theoretical chemistry at the Ruhr-University Bochum where he received his PhD in 2015 for the development of machine learning potentials for efficient QM simulations. After postdoctoral stays at the University of Vienna and Stanford University, he joined Bayer AG in 2019 as Computational Life Scientist contributing to the IMI funded project MELLODDY on federated privacy-preserving machine learning. He is excited about exploring the synergies between different computational approaches and co-leads the “!AIQU” project which aims to combine quantum chemical calculations with machine learning for rapid and accurate in silico predictions at large scale.

Dr Lewis MERVIN

ASTRAZENECA, Cambridge, United Kingdom
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Dr Lewis MERVIN

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Lewis Mervin obtained his PhD from the University of Cambridge in 2017 through a BBSRC/AstraZeneca CASE studentship, working on the development of novel algorithms for mode-of-action analysis. He spent two years as a postdoctoral researcher studying the systems biology of alcohol addiction (Sybil-AA). In 2019, Lewis joined AstraZeneca as a Machine Learning and Cheminformatics Expert in the Molecular AI team lead by Ola Engkvist.