Advanced machine learning for Innovative Drug Discovery

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Software for retrosynthesis

Publicly available source code for chemical retrosynthesis

First newsletter

Publication of the first newsletter

ML toolbox for QM property prediction

Open source Machine Learning (ML) toolbox for Quantum Mechanics (QM) property prediction

One Chemistry model

"Source code of the ""One Chemistry"" that combines individual developments of others ESRs"

Modular AI systems

Methodology and source code for robust training of modular AI systems

Software for phenotypic screening

Open source software for phenotypic screening predictions with confidence estimation

Use of human knowledge for models development

Methodology to improve machine learning models in drug discovery with human knowledge

SS2 school report
Recruitment meeting

Minutes of the recruitment meeting

Minutes of the kick-off meeting
SS3 school report
Communication and public engagement activities

Overview of communication and public engagement activities

Final conference

Final conference report

SS1 school report

Web site

The web site and web application system is established

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Publications

Equivariant Graph Neural Networks for Toxicity Prediction

Auteurs: Julian Cremer, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Djork-Arné Clevert, Gianni De Fabritiis
Publié dans: Chemical Research in Toxicology, 2023, ISSN 0893-228X
Éditeur: American Chemical Society
DOI: 10.1021/acs.chemrestox.3c00032

Developing novel Lin28 inhibitors by computer aided drug design

Auteurs: Xuesen Dong, Victor Barrios, Mariia Radaeva, Graciella Rosellinny, Qiongqiong Jia, Ning Xie, Jason Smith, Martin Gleave, Nada Lallous, Artem Cherkasov, Hanadi Ibrahim, Monica Villanueva, Suzana Straus
Publié dans: Cell Death Discovery, 2025, ISSN 2058-7716
Éditeur: Nature Publishing Group
DOI: 10.21203/rs.3.rs-4644460/v1

The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge

Auteurs: Andrea Hunklinger, Peter Hartog, Martin Šícho, Guillaume Godin, Igor V. Tetko
Publié dans: SLAS Discovery, Numéro 29, 2024, Page(s) 100144, ISSN 2472-5552
Éditeur: Elsevier BV
DOI: 10.1016/j.slasd.2024.01.005

HyperPCM: Robust Task-Conditioned Modeling of Drug–Target Interactions

Auteurs: Emma Svensson, Pieter-Jan Hoedt, Sepp Hochreiter, Günter Klambauer
Publié dans: Journal of Chemical Information and Modeling, Numéro 64, 2024, Page(s) 2539-2553, ISSN 1549-9596
Éditeur: American Chemical Society
DOI: 10.1021/acs.jcim.3c01417

Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition

Auteurs: Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko
Publié dans: Journal of Cheminformatics, 2024, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.26434/chemrxiv-2024-kdx3g

Modeling noncovalent interatomic interactions on a photonic quantum computer

Auteurs: Matthieu Sarkis, Alessio Fallani, Alexandre Tkatchenko
Publié dans: Physical Review Research, Numéro 5, 2023, ISSN 2643-1564
Éditeur: American Physical Society (APS)
DOI: 10.1103/physrevresearch.5.043072

Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling

Auteurs: Alessio Fallani, Ramil Nugmanov, Jose Arjona-Medina, Jörg Kurt Wegner, Alexandre Tkatchenko, Kostiantyn Chernichenko
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-00970-0

AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models

Auteurs: Andrius Bernatavicius, Martin Šícho, Antonius P. A. Janssen, Alan Kai Hassen, Mike Preuss, Gerard J. P. van Westen
Publié dans: Journal of Chemical Information and Modeling, Numéro 64, 2024, Page(s) 8113-8122, ISSN 1549-9596
Éditeur: American Chemical Society
DOI: 10.1021/acs.jcim.4c00309

Investigations into the efficiency of computer-aided synthesis planning

Auteurs: Peter B.R. Hartog, Annie M. Westerlund, Igor V. Tetko, Samuel Genheden
Publié dans: Journal of Chemical Information and Modeling, 2024, ISSN 1549-9596
Éditeur: American Chemical Society
DOI: 10.26434/chemrxiv-2024-q2v87

Unsupervised Representation Learning for Proteochemometric Modeling

Auteurs: Kim, PT., Winter, R., Clevert, DA.
Publié dans: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2021, ISSN 1422-0067
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)

Improving route development using convergent retrosynthesis planning

Auteurs: Paula Torren-Peraire, Jonas Verhoeven, Dorota Herman, Hugo Ceulemans, Igor V. Tetko, Jörg K. Wegner
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-00953-1

Development of Novel Inhibitors Targeting the D-Box of the DNA Binding Domain of Androgen Receptor

Auteurs: Radaeva, M., Ban, F., Zhang, F., LeBlanc, E., Lallous, N., Rennie, P.S., Gleave, M.E., Cherkasov, A.
Publié dans: Int. J. Mol. Sci., 2021, Page(s) 2493, ISSN 1422-0067
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/ijms22052493

Img2Mol – accurate SMILES recognition from molecular graphical depictions

Auteurs: Clevert, DA., Le, T., Winter, R., Montanari, F.
Publié dans: CHEMICAL SCIENCE, 2021, ISSN 2041-6520
Éditeur: Royal Society of Chemistry
DOI: 10.1039/d1sc01839f

Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design

Auteurs: Alan Kai Hassen, Martin Šícho, Yorick J. van Aalst, Mirjam C. W. Huizenga, Darcy N. R. Reynolds, Sohvi Luukkonen, Andrius Bernatavicius, Djork-Arné Clevert, Antonius P. A. Janssen, Gerard J. P. van Westen, Mike Preuss
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-024-00910-4

Be aware of overfitting by hyperparameter optimization!

Auteurs: Igor V. Tetko, Ruud van Deursen, Guillaume Godin
Publié dans: Journal of Cheminformatics, Numéro 16, 2024, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-024-00934-w

The Promise of AI for DILI Prediction

Auteurs: Vall, A., Sabnis, Y., Shi, J., Class, R., Hochreiter, S., Klambauer, G.
Publié dans: Front. Artif. Intell., 2021, ISSN 0922-6389
Éditeur: IOS Press
DOI: 10.3389/frai.2021.638410

Expanding the chemical space using a chemical reaction knowledge graph

Auteurs: Emma Rydholm, Tomas Bastys, Emma Svensson, Christos Kannas, Ola Engkvist, Thierry Kogej
Publié dans: Digital Discovery, Numéro 3, 2024, Page(s) 1378-1388, ISSN 2635-098X
Éditeur: Royal Society of Chemistry (RSC)
DOI: 10.1039/d3dd00230f

Tackling assay interference associated with small molecules

Auteurs: Lu Tan, Steffen Hirte, Vincenzo Palmacci, Conrad Stork, Johannes Kirchmair
Publié dans: Nature Reviews Chemistry, Numéro 8, 2024, Page(s) 319-339, ISSN 2397-3358
Éditeur: Springer Science and Business Media LLC
DOI: 10.1038/s41570-024-00593-3

Online OCHEM multi-task model for solubility and lipophilicity prediction of platinum complexes

Auteurs: Nesma Mousa, Hristo P. Varbanov, Vidya Kaipanchery, Elisabetta Gabano, Mauro Ravera, Andrey A. Toropov, Larisa Charochkina, Filipe Menezes, Guillaume Godin, Igor V. Tetko
Publié dans: Journal of Inorganic Biochemistry, Numéro 269, 2025, Page(s) 112890, ISSN 0162-0134
Éditeur: Elsevier BV
DOI: 10.1016/j.jinorgbio.2025.112890

CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures

Auteurs: Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer
Publié dans: Nature Communications, Numéro 14, 2023, ISSN 2041-1723
Éditeur: Nature Publishing Group
DOI: 10.1038/s41467-023-42328-w

Statistical approaches enabling technology-specific assay interference prediction from large screening data sets

Auteurs: Vincenzo Palmacci, Steffen Hirte, Jorge Enrique Hernández González, Floriane Montanari, Johannes Kirchmair
Publié dans: Artificial Intelligence in the Life Sciences, Numéro 5, 2024, Page(s) 100099, ISSN 2667-3185
Éditeur: Elsevier BV
DOI: 10.1016/j.ailsci.2024.100099

Multi-objective synthesis planning by means of Monte Carlo Tree search

Auteurs: Helen Lai, Christos Kannas, Alan Kai Hassen, Emma Granqvist, Annie M. Westerlund, Djork-Arné Clevert, Mike Preuss, Samuel Genheden
Publié dans: Artificial Intelligence in the Life Sciences, Numéro 7, 2025, Page(s) 100130, ISSN 2667-3185
Éditeur: Elsevier BV
DOI: 10.1016/j.ailsci.2025.100130

Network Analysis of the Organic Chemistry in Patents, Literature, and Pharmaceutical Industry

Auteurs: Emma Svensson, Emma Granqvist, Tomas Bastys, Christos Kannas, Mikhail Kabeshov, Samuel Genheden, Ola Engkvist, Thierry Kogej
Publié dans: Molecular Informatics, Numéro 44, 2025, ISSN 1868-1743
Éditeur: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.202500011

Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design

Auteurs: Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-00986-6

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

Auteurs: Leonardo Medrano Sandonas, Dries Van Rompaey, Alessio Fallani, Mathias Hilfiker, David Hahn, Laura Perez-Benito, Jonas Verhoeven, Gary Tresadern, Joerg Kurt Wegner, Hugo Ceulemans, Alexandre Tkatchenko
Publié dans: Scientific Data, Numéro 11, 2024, ISSN 2052-4463
Éditeur: Springer Science and Business Media LLC
DOI: 10.1038/s41597-024-03521-8

Inverse mapping of quantum properties to structures for chemical space of small organic molecules

Auteurs: Alessio Fallani, Leonardo Medrano Sandonas, Alexandre Tkatchenko
Publié dans: Nature Communications, Numéro 15, 2024, ISSN 2041-1723
Éditeur: Nature Publishing Group
DOI: 10.1038/s41467-024-50401-1

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Auteurs: Roland, T., Bock, C., Tschoellitsch, T., Maletzky, A., Hochreiter, S., Meier, J., Klambauer, G.
Publié dans: JOURNAL OF MEDICAL SYSTEMS, 2022, ISSN 0148-5598
Éditeur: Kluwer Academic/Plenum Publishers
DOI: 10.1007/s10916-022-01807-1

Models Matter: the impact of single-step retrosynthesis on synthesis planning

Auteurs: Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden, Jonas Verhoeven, Djork-Arné Clevert, Mike Preuss, Igor V. Tetko
Publié dans: Digital Discovery, Numéro 3, 2024, Page(s) 558-572, ISSN 2635-098X
Éditeur: Royal Society of Chemistry (RSC)
DOI: 10.1039/d3dd00252g

Decoding phenotypic screening: A comparative analysis of image representations

Auteurs: Adriana Borowa, Dawid Rymarczyk, Marek Żyła, Maciej Kańduła, Ana Sánchez-Fernández, Krzysztof Rataj, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
Publié dans: Computational and Structural Biotechnology Journal, Numéro 23, 2025, Page(s) 1181-1188, ISSN 2001-0370
Éditeur: Elsevier BV
DOI: 10.1016/j.csbj.2024.02.022

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall-runoff modeling

Auteurs: Kratzert, F., Klotz, D., Hochreiter, S., Nearing, GS.
Publié dans: HYDROLOGY AND EARTH SYSTEM SCIENCES, 2021, ISSN 1027-5606
Éditeur: European Geophysical Society
DOI: 10.5194/hess-25-2685-2021

Accelerating the inference of string generation-based chemical reaction models for industrial applications

Auteurs: Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-00974-w

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Auteurs: Varvara Voinarovska, Mikhail Kabeshov, Dmytro Dudenko, Samuel Genheden, Igor V. Tetko
Publié dans: Journal of Chemical Information and Modeling, Numéro 64, 2024, Page(s) 42-56, ISSN 1549-9596
Éditeur: American Chemical Society
DOI: 10.1021/acs.jcim.3c01524

Metis: a python-based user interface to collect expert feedback for generative chemistry models

Auteurs: Janosch Menke, Yasmine Nahal, Esben Jannik Bjerrum, Mikhail Kabeshov, Samuel Kaski, Ola Engkvist
Publié dans: Journal of Cheminformatics, Numéro 16, 2024, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-024-00892-3

Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning

Auteurs: Tuan Le, Julian Cremer, Djork-Arné Clevert, Kristof T. Schütt
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-01028-x

Low Concentration Cell Painting Images Enable the Identification of Highly Potent Compounds

Auteurs: Son V. Ha, Steffen Jaensch, Lorena G. A. Freitas, Dorota Herman, Paul Czodrowski, Hugo Ceulemans
Publié dans: Scientific Reports, 2024, ISSN 2045-2322
Éditeur: Nature Publishing Group
DOI: 10.21203/rs.3.rs-4466969/v1

Temporal distribution shift in real-world pharmaceutical data: Implications for uncertainty quantification in QSAR models

Auteurs: Hannah Rosa Friesacher, Emma Svensson, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist
Publié dans: Artificial Intelligence in the Life Sciences, Numéro 8, 2025, Page(s) 100132, ISSN 2667-3185
Éditeur: Elsevier BV
DOI: 10.1016/j.ailsci.2025.100132

Rainfall-runoff prediction at multiple timescales with a single Long Short-Term Memory network

Auteurs: Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., Hochreiter, S.
Publié dans: HYDROLOGY AND EARTH SYSTEM SCIENCES, 2021, ISSN 1027-5606
Éditeur: European Geophysical Society
DOI: 10.5194/hess-25-2045-2021

Reagent Prediction with a Molecular Transformer Improves Reaction Data Quality

Auteurs: Andronov, M., Voinarovska, V., Andronova, N., Wand, M., Clevert, D.-A., Schmidhuber, J.
Publié dans: Chemical Science, 2023, ISSN 2573-2293
Éditeur: Royal Society of Chemistry
DOI: 10.1039/d2sc06798f

FSL-CP: a benchmark for small molecule activity few-shot prediction using cell microscopy images

Auteurs: Son V. Ha, Lucas Leuschner, Paul Czodrowski
Publié dans: Digital Discovery, Numéro 3, 2024, Page(s) 719-727, ISSN 2635-098X
Éditeur: Royal Society of Chemistry (RSC)
DOI: 10.1039/d3dd00205e

Enhancing uncertainty quantification in drug discovery with censored regression labels

Auteurs: Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist
Publié dans: Artificial Intelligence in the Life Sciences, Numéro 7, 2025, Page(s) 100128, ISSN 2667-3185
Éditeur: Elsevier BV
DOI: 10.1016/j.ailsci.2025.100128

Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models

Auteurs: Hannah Rosa Friesacher, Ola Engkvist, Lewis Mervin, Yves Moreau, Adam Arany
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-00964-y

E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays

Auteurs: Vincenzo Palmacci, Yasmine Nahal, Matthias Welsch, Ola Engkvist, Samuel Kaski, Johannes Kirchmair
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-01014-3

AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application

Auteurs: Lakshidaa Saigiridharan, Alan Kai Hassen, Helen Lai, Paula Torren-Peraire, Ola Engkvist, Samuel Genheden
Publié dans: Journal of Cheminformatics, Numéro 16, 2024, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-024-00860-x

Uncertainty estimation with deep learning for rainfall-runoff modeling

Auteurs: Klotz, D., Kratzert, F., Gauch, M., Sampson, AK., Brandstetter, J., Klambauer, G., Hochreiter, S., Nearing, G.
Publié dans: HYDROLOGY AND EARTH SYSTEM SCIENCES, 2022, ISSN 1027-5606
Éditeur: European Geophysical Society
DOI: 10.5194/hess-26-1673-2022

Development of Novel Inhibitors Targeting the D-Box of the DNA Binding Domain of Androgen Receptor

Auteurs: Mariia Radaeva, Fuqiang Ban, Fan Zhang, Eric LeBlanc, Nada Lallous, Paul S. Rennie, Martin E. Gleave, Artem Cherkasov
Publié dans: International Journal of Molecular Sciences, Numéro 22, 2025, Page(s) 2493, ISSN 1422-0067
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/ijms22052493

Human-in-the-loop active learning for goal-oriented molecule generation

Auteurs: Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski
Publié dans: Journal of Cheminformatics, Numéro 16, 2024, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-024-00924-y

Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling

Auteurs: Son V. Ha, Steffen Jaensch, Maciej M. Kańduła, Dorota Herman, Paul Czodrowski, Hugo Ceulemans
Publié dans: Scientific Reports, Numéro 15, 2025, ISSN 2045-2322
Éditeur: Nature Publishing Group
DOI: 10.1038/s41598-025-05914-0

PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling

Auteurs: Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
Publié dans: Chemical Science, Numéro 15, 2024, Page(s) 14954-14967, ISSN 2041-6520
Éditeur: Royal Society of Chemistry
DOI: 10.1039/d4sc03523b

Synergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research

Auteurs: Alessio Fallani
Publié dans: PhD dissertation, 2024
Éditeur: University of Luxembourg Open Repository and Bibliography

Development of small molecule inhibitors of protein nucleic acid interactions with the use of computer-aided drug discovery tools

Auteurs: Mariia Radaeva
Publié dans: Doctoral Thesis, 2024
Éditeur: University of British Columbia Library
DOI: 10.14288/1.0445334

Current Limitations of Text-based Molecular Representations for Machine Learning in Small Molecule Drug Discovery

Auteurs: Peter Bart Rudolf Hartog
Publié dans: Doctoral thesis, 2025
Éditeur: TUM School of Life Sciences

Prediction of yields of chemical reactions

Auteurs: Varvara Voinarovska
Publié dans: Doctoral thesis, 2024
Éditeur: TUM School of Natural Sciences

Equivariant graph neural networks in drug discover: from property prediction to molecule generation

Auteurs: Julian Cremer
Publié dans: PhD dissertation, 2024, Page(s) 147
Éditeur: Universitat Pompeu Fabra

Computer-aided synthesis planning for real-world applications

Auteurs: Paula Torren Peraire
Publié dans: Doctoral thesis, 2025
Éditeur: TUM School of Natural Sciences

Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction

Auteurs: Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden, Jonas Verhoeven, Mike Preuss, Igor V. Tetko
Publié dans: NeurIPS 2022 Workshop AI4Science, 2022
Éditeur: OpenReview.net

Robust Task-Specific Adaption of Models for Drug-Target Interaction Prediction

Auteurs: Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G.
Publié dans: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022
Éditeur: NeurIPS 2022
DOI: 10.5281/zenodo.8138500

Atom-Level Optical Chemical Structure Recognition with Limited Supervision

Auteurs: Martijn Oldenhof, Edward De Brouwer, Adam Arany, Yves Moreau
Publié dans: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, Page(s) 17669-17678
Éditeur: IEEE
DOI: 10.1109/cvpr52733.2024.01673

Multi-Modal Representation learning for molecules

Auteurs: Muhammad Arslan Masood, Markus Heinonen, Samuel Kaski
Publié dans: ICLR 2025 Workshop LMRL, 2025
Éditeur: OpenReview.net
DOI: 10.5281/zenodo.15484224

Leveraging expert feedback to align proxy and ground truth rewards in goal-oriented molecular generation

Auteurs: Julien Martinelli, Yasmine Nahal, Duong Lê, Ola Engkvist, Samuel Kaski
Publié dans: NeurIPS 2023 AI for Drug Discovery and Development (AI4D3) workshop, 2023, Page(s) 1-16
Éditeur: NeurIPS

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Auteurs: Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
Publié dans: ICLR 2024, 2024
Éditeur: arXiv
DOI: 10.48550/arxiv.2309.17296

Contrastive Learning of Image- and Structure-Based Representations in Drug Discovery

Auteurs: Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S., Klambauer, G.
Publié dans: International Conference on Learning Representations, 2022
Éditeur: MLDD workshop, ICLR 2022
DOI: 10.5281/zenodo.8137823

MC-LSTM: Mass-Conserving LSTM

Auteurs: Hoedt, PJ., Kratzert, F., Klotz, D., Halmich, C., Holzleitner, M., Nearing, G., Hochreiter, S., Klambauer, G.
Publié dans: INTERNATIONAL CONFERENCE ON MACHINE LEARNING, 2021, ISSN 2640-3498
Éditeur: Cambridge MA: JMLR
DOI: 10.5281/zenodo.8138359

Expressive Graph Informer Networks

Auteurs: Simm, J., Arany, A., De Brouwer, E., Moreau, Y.
Publié dans: MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT II, Book Series Title: Lecture Notes in Computer Science, 2022, ISSN 0302-9743
Éditeur: Springer Verlag
DOI: 10.1007/978-3-030-95470-3_15

Synthesis Planning in Reaction Space: A Study on Success, Robustness and Diversity

Auteurs: Alan Kai Hassen, Helen Lai, Samuel Genheden, Mike Preuss, Djork-Arné Clevert
Publié dans: ChemRxiv Preprint, 2025
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2025-js7dt

A reagent-driven visual method for analyzing chemical reaction data

Auteurs: Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert
Publié dans: arXiv preprint, 2024
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2024-q9tc4

Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search

Auteurs: Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert
Publié dans: arXiv preprint, 2025
Éditeur: arXiv
DOI: 10.48550/arxiv.2508.01459

Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer

Auteurs: Sarkis, M., Fallani, A.,Tkatchenko, A.
Publié dans: 2023
Éditeur: arXiv
DOI: 10.48550/arxiv.2306.08544

Which modern AI methods provide accurate predictions of toxicological endpoints? Analysis of Tox24 challenge results.

Auteurs: Stephanie A. Eytcheson, Igor V. Tetko
Publié dans: 2025
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2025-7k7x3

NETWORK ANALYSIS OF THE ORGANIC CHEMISTRY IN PATENTS, LITERATURE, AND PHARMACEUTICAL INDUSTRY

Auteurs: Thierry Kogej, Emma Svensson, Emma Rydholm, Tomas Bastys, Christos Kannas, Mikhail Kabeshov, Samuel Genheden, Ola Engkvist
Publié dans: Chemrxiv, Numéro Chemrxiv, 2024
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2024-h4qlt

openOCHEM consensus model wins Kaggle First EUOS/SLAS Joint Compound Solubility Challenge

Auteurs: Kopp, A., Hartog, P., Šícho, M., Godin, G., Tetko, I.V.
Publié dans: 2023
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2023-p8qcv

Equivariant Graph Neural Networks for Toxicity Prediction

Auteurs: Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.A., De Fabritiis, G.
Publié dans: 2023, ISSN 2573-2293
Éditeur: ChemRxiv
DOI: 10.26434/chemrxiv-2023-9kb55

Temporal Evaluation of Probability Calibration with Experimental Errors

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Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 13-20
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_2

Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation

Auteurs: Julian Cremer, Tuan Le, Djork-Arné Clevert, Kristof T. Schütt
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 36-46
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_4

Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer

Auteurs: Alessio Fallani, José Arjona-Medina, Konstantin Chernichenko, Ramil Nugmanov, Jörg Kurt Wegner, Alexandre Tkatchenko
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 71-81
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_7

Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery

Auteurs: Yasmine Nahal, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 58-70
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_6

Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss

Auteurs: Muhammad Arslan Masood, Samuel Kaski, Hugo Ceulemans, Dorota Herman, Markus Heinonen
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 82-97
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_8

Curating Reagents in Chemical Reaction Data with an Interactive Reagent Space Map

Auteurs: Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 21-35
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_3

Temporal Evaluation of Uncertainty Quantification Under Distribution Shift

Auteurs: Emma Svensson, Hannah Rosa Friesacher, Adam Arany, Lewis Mervin, Ola Engkvist
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 132-148
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_11

Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules

Auteurs: Mathias Hilfiker, Leonardo Medrano Sandonas, Marco Klähn, Ola Engkvist, Alexandre Tkatchenko
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 47-57
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_5

Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse

Auteurs: Peter B. R. Hartog, Emma Svensson, Lewis Mervin, Samuel Genheden, Ola Engkvist, Igor V. Tetko
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 98-115
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_9

Deep Bayesian Experimental Design for Drug Discovery

Auteurs: Muhammad Arslan Masood, Tianyu Cui, Samuel Kaski
Publié dans: Lecture Notes in Computer Science, AI in Drug Discovery, 2024, Page(s) 149-159
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-72381-0_12

Introduction to the Special Issue: AI Meets Toxicology

Auteurs: Günter Klambauer, Djork-Arné Clevert, Imran Shah, Emilio Benfenati, Igor V. Tetko
Publié dans: Chemical Research in Toxicology, Numéro 36, 2023, Page(s) 1163-1167, ISSN 0893-228X
Éditeur: American Chemical Society
DOI: 10.1021/acs.chemrestox.3c00217

Tox24 Challenge

Auteurs: Igor V. Tetko
Publié dans: Chemical Research in Toxicology, Numéro 37, 2024, Page(s) 825-826, ISSN 0893-228X
Éditeur: American Chemical Society
DOI: 10.1021/acs.chemrestox.4c00192

Which Modern AI Methods Provide Accurate Predictions of Toxicological End Points? Analysis of Tox24 Challenge Results

Auteurs: Stephanie A. Eytcheson, Igor V. Tetko
Publié dans: Chemical Research in Toxicology, 2025, ISSN 0893-228X
Éditeur: American Chemical Society
DOI: 10.1021/acs.chemrestox.5c00273

Advanced machine learning for innovative drug discovery

Auteurs: Igor V. Tetko, Djork-Arné Clevert
Publié dans: Journal of Cheminformatics, Numéro 17, 2025, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-025-01061-w

S-07-03 A snapshot of AI in predictive toxicology: explainable AI

Auteurs: I.V. Tetko
Publié dans: Toxicology Letters, Numéro 368, 2022, Page(s) S25-S26, ISSN 0378-4274
Éditeur: Elsevier BV
DOI: 10.1016/j.toxlet.2022.07.085

S02-04 Computational Toxicology in Drug Safety

Auteurs: I. Tetko
Publié dans: Toxicology Letters, Numéro 399, 2024, Page(s) S14, ISSN 0378-4274
Éditeur: Elsevier BV
DOI: 10.1016/j.toxlet.2024.07.047

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