Accepted papers

Spotlight papers

Spotlight presentations - Part 1 (10:30-10:50am ET)

1. Deep sharpening of topological features for de novo protein design

Authors: Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, Pierre Vandergheynst, Micheal Bronstein, Bruno Correia

2. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Authors: Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola

3. Predicting single-cell perturbation responses for unseen drugs

Authors: Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J Theis

4. GRPE: Relative Positional Encoding for Graph Transformer

Authors: Wonpyo Park, Woong-Gi Chang, Donggeon Lee, Juntae Kim, seung-won hwang

Spotlight presentations - Part 2 (3:40-4:00pm ET)

5. SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching

Authors: Scott Gigante, Varsha Raghavan, Amanda M. Robinson, Rob Barton, Adeeb H. Rahman, Drausin F. Wulsin, Jacques Banchereau, Noam Solomon, Luis F. Voloch, Fabian J Theis

6. Physics-informed deep neural network for rigid-body protein docking

Authors: Freyr Sverrisson, Jean Feydy, Joshua Southern, Michael M. Bronstein, Bruno Correia

7. Multi-Segment Preserving Sampling for Deep Manifold Sampler

Authors: Dan Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Richard Bonneau, Vladimir Gligorijevic, Stephen Ra, Kyunghyun Cho

8. Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens

Authors: Jannis Born, Matteo Manica

Accepted posters

Poster session 1 (12-12:45pm ET)

1. SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching

Authors: Scott Gigante, Varsha Raghavan, Amanda M. Robinson, Rob Barton, Adeeb H. Rahman, Drausin F. Wulsin, Jacques Banchereau, Noam Solomon, Luis F. Voloch, Fabian J Theis

2. Contrastive learning of image- and structure-based representations in drug discovery

Authors: Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer

3. Physics-informed deep neural network for rigid-body protein docking

Authors: Freyr Sverrisson, Jean Feydy, Joshua Southern, Michael M. Bronstein, Bruno Correia

4. Graph Anisotropic Diffusion for Molecules

Authors: Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Micha el M. Bronstein

5. Predicting single-cell perturbation responses for unseen drugs

Authors: Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J Theis

6. An evaluation framework for the objective functions of de novo drug design benchmarks

Authors: Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato

7. DebiasedDTA: Model Debiasing to Boost Drug-Target Affinity Prediction

Authors: Rıza Özçelik, Alperen Bağ, Berk Atıl, Arzucan Özgür, Elif Ozkirimli

8. Glolloc: Mixture of Global and Local Experts for Molecular Activity Prediction

Authors: Héléna A. Gaspar, Matthew P. Seddon

9. The Rosenbluth sampling Calculation of Hydrophobic-Polar Model

Authors: Marcin Józef Wierzbinski, Alessandro Crimi

10. Prediction of molecular field points using se(3)-transformer model

Authors: Florian Hinz, Amr H Mahmoud, Markus Alexander Lill

11. Decoding Surface Fingerprints for Protein-Ligand Interactions

Authors: Ilia Igashov, Arian Rokkum Jamasb, Ahmed Sadek, Freyr Sverrisson, Arne Schneuing, Tom Blundell, Pietro Lio, Michael M. Bronstein, Bruno Correia

12. High-Content Similarity-Based Virtual Screening Using a Distance Aware Transformer Model

Authors: Manuel Sebastian Sellner, Amr H Mahmoud, Markus Alexander Lill

13. Evaluating Generalization in GFlowNets for Molecule Design

Authors: Andrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M. Bronstein, Yoshua Bengio

14. Deep Learning Model for Flexible and Efficient Protein-Ligand Docking

Authors: Matthew Masters, Amr H Mahmoud, Yao Wei, Markus Alexander Lil

15. Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens

Authors: Jannis Born, Matteo Manica

16. Machine Learning to Hunt for Phage Proteins to Catch Klebsiella

Authors: George Wright, Fayyaz ul Amir Afsar Minhas, Slawomir Michniewski, Eleanor Jameson

17. Deep sharpening of topological features for de novo protein design

Authors: Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, Pierre Vandergheynst, Micheal Bronstein, Bruno Correia

18. Improving the assessment of deep learning models in the context of drug-target interaction prediction

Authors: Mirko Torrisi, Antonio De la Vega de Leon, Guillermo Climent, Remco Loos, Alejandro Panjkovich

Poster session 2 (4:40-5:25pm ET)

1. Isolating salient variations of interest in single-cell transcriptomic data with contrastive VI

Authors: Ethan Weinberger, Chris Lin, Su-In Lee

2. ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration

Authors: Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang, Bolei Zhou

3. Benchmarking Uncertainty Quantification for Protein Engineering

Authors: Kevin P. Greenman, Ava Soleimany, Kevin K Yang

4. Convolutions are competitive with transformers for protein sequence pretraining

Authors: Kevin K Yang, Alex Xijie Lu, Nicolo Fusi

5. MetaDTA: Meta-learning-based drug-target binding affinity prediction

Authors: Eunjoo Lee, Jiho Yoo, Huisun Lee, Seunghoon Hong

6. Partial Product Aware Machine Learning on DNA-Encoded Libraries

Authors: Polina Binder, Meghan Lawler, LaShadric Grady, Neil Carlson, Svetlana Belyanskaya, Joe Franklin, Nicolas Tilmans, Henri Palacci

7. De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning

Authors: Andrew D. McNaughton, Carter Knutson, Mridula Bontha, Jenna A. Pope, Neeraj Kumar

8. Torsional Diffusion for Molecular Conformer Generation

Authors: Bowen Jing, Gabriele Corso, Regina Barzilay, Tommi S. Jaakkola

9. Fragment-based ligand generation guided by geometric deep learning on protein-ligand structures

Authors: Alexander S Powers, Helen H. Yu, Patricia Adriana Suriana, Ron O. Dror

10. Data-Driven Optimization for Protein Design: Workflows, Algorithms and Metrics

Authors: Sathvik Kolli, Amy X. Lu, Xinyang Geng, Aviral Kumar, Sergey Levine

11. Variational Interpretable Deep Canonical Correlation Analysis

Authors: Lin Qiu, Vernon M. Chinchilli, Lin Lin

12. Auto-regressive WaveNet Variational Autoencoders for Alignment-free Generative Protein Design and Fitness Prediction

Authors: Niksa Praljak, Andrew Ferguson

13. Learning multi-scale functional representations of proteins from single-cell microscopy data

Authors: Anastasia Razdaibiedina, Alexander Brechalov

14. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Authors: Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola

15. GRPE: Relative Positional Encoding for Graph Transformer

Authors: Wonpyo Park, Woong-Gi Chang, Donggeon Lee, Juntae Kim, seung-won hwang

16. Multi-Segment Preserving Sampling for Deep Manifold Sampler

Authors: Dan Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Richard Bonneau, Vladimir Gligorijevic, Stephen Ra, Kyunghyun Cho