Session RED
Analysis of Large Chemical Data Sets
| P01 | HASTENing structure-based virtual screening of large chemical libraries Tuomo Sakari Kalliokoski  , Orion Pharma PDF | ||
| P03 | DEL design at Ryvu Marcin A. Król, Ryvu Therapeutics | ||
| P05 | 40 million PubChem structures from patents: both treasure trove and junk yard Christopher Southan , Medicines Discovery Catapult PDF |
Artificial Intelligence Approaches
| P07 | Artificial Intelligence for Compound Design and Automation of DMTA Cycles Susanne Sauer, Sanofi-Aventis Deutschland GmbH | ||
| P09 | Multi-target uncertainty quantification for de novo drug design Sohvi Iiri Maria Luukkonen , Leiden University | ||
| P11 | Planning of chemical synthesis of focused libraries of similars to a given compound Adeliia Fatykhova, Kazan Federal University | ||
| P13 | MoleculeACE: a benchmark for machine learning with activity cliffs Derek van Tilborg , Eindhoven University of Technology | ||
| P15 | The chemistry puppeteer: enhancing the diversity of single-step retrosynthesis Alessandra Toniato , IBM Research Europe |
Cheminformatics Approaches
| P17 | GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics (updates and perspective) Martin Šícho , Leiden University | ||
| P19 | Applying machine learning for virtual drug discovery and development of adenosine A2A ligands combining in silico medicinal chemistry and quantitative systems pharmacology Helle Willemijn Van Den Maagdenberg, Leiden University | ||
| P21 | Combining shape and electrostatics in a spectral geometry-based 3D molecular descriptor James Alexander Middleton, University of Sheffield | ||
| P23 | Using Matched Molecular Pairs for CoreDesign® Jess Stacey, MedChemica Ltd | ||
| P25 | The Future of InChI Gerd Blanke , StructurePendium Technologies GmbH |
Dealing with Biological Complexity
| P27 | PKD-KG: A drug repurposing knowledge graph for Autosomal Dominant Polycystic Kidney Disease (ADPKD) Bola Khalil  , Janssen Pharmaceutica / Leiden University | ||
| P29 | Molecular dynamics-based elucidation of Flap endonuclease 1 flexibility for DNA cleavage Zied Hosni, University of Sheffield |
Structure-Activity and Structure-Property Prediction
| P31 | Testing the limits of prediction in QSPR models considering their applicability domain Modest von Korff, Idorsia Pharmaceuticals Ltd | ||
| P33 | Predictive-based selection of drug candidates for Autosomal Dominant Polycystic Kidney Disease (ADPKD) David Figueiredo Vidal , Leiden University | ||
| P35 | Virtual Distillation of Naphthas Using Molecular Property Prediction Algorithms Maarten R. Dobbelaere, Ghent University | ||
| P37 | Use of semi-quantitative (censored) data for QSAR modeling of hERG inhibitory potency Andrius Sazonovas, VsI Aukstieji Algoritmai PDF | ||
| P39 | DFT and ML modeling of peptide properties for cytotoxicity prediction Anzhelika Markovnikova, ITMO University |
Structure-Based Approaches
| P41 | Conservation Analysis of anti-TB Target DnaE1 and Identification of Potential Interactions of DnaE1 Inhibitor Nargenicin on the Human Proteome Rosan Kuin, Leiden University | ||
| P43 | Tracing the difference: Comparative modeling of human Uridine 5’-diphosphoglucuronosyltransferase guided by molecular dynamics simulations Sijie Liu, Freie Universität Berlin | ||
| P45 | Structural Investigations of Protein Kinases with GeoMine Christiane Ehrt , Universität Hamburg PDF | ||
| P47 | Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses Andreas Luttens, Uppsala University | ||
| P49 | GenCReM: de novo generation of synthetically feasible compounds based on genetic algorithm Aleksandra Ivanová , Institute of Molecular and Translational Medicine | ||
| P51 | MD pharmacophore-based search for novel MARK4 inhibitors Alina Kutlushina, Palacky University |
Session BLUE
Analysis of Large Chemical Data Sets
| P02 | Ring systems in natural products: structural diversity, physicochemical properties, and coverage by synthetic compounds Ya Chen , University of Vienna | ||
| P04 | Utilizing the semantic web and network tools to integrate pharmacokinetic, -dynamic, and OMICS data with metabolic (disease) pathways Denise Slenter , Maastricht University |
Artificial Intelligence Approaches
| P06 | The DECIMER (Deep lEarning for Chemical ImagE Recognition) project Kohulan Rajan  , Friedrich Schiller University, code | ||
| P08 | New approaches for antimicrobial peptides prediction using Machine-Learning Colin Titouan Bournez, Leiden University | ||
| P10 | Application of DeepSMILES to machine-learning of chemical structures Noel Michael O’Boyle , Sosei Heptares PPTX | ||
| P12 | Towards Predicting Enzyme Activity by Traversing Biomedical Knowledge Graphs Terence Egbelo, University of Sheffield PDF | ||
| P14 | TERP: a machine learning approach for predicting and prioritizing specialized metabolite tailoring enzyme products David Meijer , Wageningen University | ||
| P16 | Enzeptional: enzyme optimization via a generative language modeling-based evolutionary algorithm Yves Gaetan Nana Teukam , IBM Research Europe |
Cheminformatics Approaches
| P18 | Algorithmic Advances in Diverse Fingerprint Selection Andrew Dalke, Andrew Dalke Scientific | ||
| P20 | Human Pharmacokinetic Prediction using Predicted Animal Pharmacokinetic Parameters and Computed Physicochemical Properties Srijit Seal , University of Cambridge | ||
| P22 | Prediction of new active ligands for the Vitamin D Receptor Maria Isabel Agea Lorente, University of Chemistry and Technology Prague | ||
| P24 | Reaction InChI: Present and Future Gerd Blanke , StructurePendium Technologies GmbH | ||
| P26 | PIKAChU: a Python-based Informatics Kit for Analysing CHemical Units Barbara R. Terlouw , Wageningen University |
Dealing with Biological Complexity
| P28 | Building classifiers to link hepatic transcriptomic profile in humans with varying degree of hepatic fibrosis Manuel Alejandro Gonzalez Hernandez , Leiden University | ||
| P30 | An automated workflow to expand AOP-Wiki Stressor chemical knowledge and identify potential activators of Adverse Outcome Pathways Marvin Martens , Maastricht University |
Structure-Activity and Structure-Property Prediction
| P32 | Exploring aspartic protease inhibitor binding to design selective antimalarials Raitis Latvian Bobrovs , Institute of Organic Synthesis | ||
| P34 | Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors Brandon J. Bongers , Leiden University | ||
| P36 | Multi-Instance Learning Approach to Predictive Modeling of Catalyst Enantioselectivity Dmitry Zankov, Kazan Federal University | ||
| P38 | VHP4Safety: building a virtual human for safety assessment Linde Schoenmaker, Universiteit Leiden |
Structure-Based Approaches
| P40 | In silico identification of dual targeting potential BACE1 and GSK-3β inhibitors for Alzheimer’s disease Nilesh Gajanan Bajad, Indian Institute of Technology, BHU, Varanasi | ||
| P42 | Atomistic insight into substrate activity of SARS-CoV-2 papain-like protease and human casein kinase 1 Laura Tesmer , Abbvie | ||
| P44 | Extracting 3D pharmacophores from molecular dynamics simulations: a case study Szymon Pach , Freie Universität Berlin | ||
| P46 | The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors Yu Chen , Freie Universität Berlin | ||
| P48 | Automated design of synthetically accessible compounds Pavlo Polishchuk , Palacky University | ||
| P50 | Structure-based generation of synthetically feasible molecules Guzel Minibaeva , Palacký University Olomouc | ||
| P52 | Automated determination of optimal λ schedules for free energy calculations Hannes Loeffler, Cresset |