Accepted Papers

Role of Structural and Conformational Diversity for Machine Learning Potentials

Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding

1

32

datamol.io - an open-source ecosystem of tools for AI-based drug discovery

Hadrien Mary, Emmanuel Noutahi, Cas Wognum, Michael Craig, Lu Zhu

1

31

Gotta be SAFE: A New Framework for Molecular Design

Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S.C Lim, Prudencio Tossou

1

30

The Discovery of Binding Modes Requires Rethinking Docking Generalization

Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi Jaakkola

2

29

Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms

Bowen Jing, Tommi Jaakkola, Bonnie Berger

1

13

AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Artem Gazizov, Anna Lian, Casper Goverde, Sergey Ovchinnikov, Nicholas Polizzi

1

21

Automated segmentation of cryo-electron tomograms

Rishwanth Raghu, S. Mazdak Abulnaga, Mahrukh Usmani, Nicolas Coudray, Gira Bhabha, Damian Ekiert, Ellen Zhong

2

18

PepPrCLIP: De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone

Suhaas Bhat, Kalyan Palepu, Pranam Chatterjee

2

23

RINGER: Conformer Ensemble Generation of Macrocyclic Peptides with Sequence-Conditioned Internal Coordinate Diffusion

Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang

1

2

Transition Path Sampling with Boltzmann Generator-based MCMC Moves

Michael Plainer, Hannes Stark, Charlotte Bunne, Stephan Gunnemann

2

28

A benchmark of dynamic molecular complexes for cryo-EM structure determination

Minkyu Jeon, Alkin Kaz, Rishwanth Raghu, Ellen D. Zhong

2

17

STRIDE: Structure-guided Generation for Inverse Design of Molecules

Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo, Kenneth Chiu

2

19

G2Retro as a two-step graph generative models for retrosynthesis prediction

Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, Xia Ning

1

16

PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling

Tianlai Chen, Sarah Pertsemlidis, Pranam Chatterjee

2

25

Accessible Molecular Machine Learning Models for the Future of Drug Discovery

Jessica G. Freeze

1

18

Learning from pre-pandemic data for the design and testing of variant-proof vaccines

Sarah Gurev1,2*, Noor Youssef1*, Nicole Thadani1*, Pascal Notin1*, Fadi Ghantous3, Kelly Brock1, Hannah Pierce-Hoffman1, Javier Jaimes4, Ann Dauphine4, Leonid Yurkovetskiy4, Daria Soto4, Ralph Estanboulieh1, Ben Kotzen5, Matteo Bosso4, Jacob Lemieux5, Jeremy Luban4, Mike Seaman3, Debora Marks1,6

1

27

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

Gang Liu, Meng Jiang

1

3

SE(3) Stochastic Flow Matching for Protein Backbone Generation

Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong

1

23

A graph representation of molecular ensembles for polymer property prediction

Matteo Aldeghi, Connor W. Coley

1

19

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio

2

7

ML Driven Photochemical Synthesis of Helicenes

Lucia Vina-Lopez, Johannes Dietschreit, Simon Axelrod, Aik Rui Tan, Vikas Vashney, Rafael Gomez-Bombarelli

1

25

A Robust, Scalable, and Versatile Approach to Ab Initio Heterogeneous Reconstruction with CryoDRGN-AI

Axel Levy, Frédéric Poitevin, Gordon Wetzstein, Ellen D Zhong

2

14

Triangular Contrastive Learning on Molecular Graphs

MinGyu Choi, Wonseok Shin, Yijingxiu Lu, Sun Kim

1

4

Local, Learned Frames for Molecules

Hannah Lawrence, Saro Passaro, Abhishek Das

2

24

DSMBind: an unsupervised generative modeling framework for binding energy prediction

Wengong Jin, Xun Chen, Amrita Vetticaden, Raktima Raychowdhury,Siranush Sarzikova, Caroline Uhler, Nir Hacohen

1

22

Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size

Albert Musaelian, Anders Johansson, Simon Batzner, Boris Kosinsky

2

12

RLSynC: Offline-Online Reinforcement Learning for Synthon Completion

Frazier N. Baker, Ziqi Chen, Xia Ning

1

14

Mapping the intermolecular interaction universe through self-supervised learning on molecular crystals

Ada Fang, Zaixi Zhang, Marinka Zitnik

1

9

Machine Learning for Molecules: A Comparative Study of Style Transfer Models

Vedika Srivastava, Hemant Kumar Singh

1

11

Multi-Fidelity Active Learning with GFlowNets

Alex Hernandez-Garcia, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

1

8

Molecular transmutation with hierarchically branched diffusion models

Alex Tseng, Tommaso Biancalani, Gabriele Scalia

1

20

Graph-Based Prediction of Biocatalyzed Reactions

Peter G. Mikhael, Itamar Chinn, Regina Barzilay

2

3

Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design

Hannes Stark, Bowen Jing, Regina Barzilay, Tommi Jaakkola

1

12

Learning Interatomic Potentials at Multiple Scales

Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky

1

28

Protein generation with evolutionary diffusion

Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X. Lu, Nicolo Fusi, Ava P. Amini, Kevin K. Yang

2

1

Structure-Infused Protein Language Models

Daniel Penaherrera, David Ryan Koes

2

11

Removing Biases from Molecular Representations via Information Maximization

Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola

1

5

Improving Graph Generation by Restricting Graph Bandwidth

Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia

1

7

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Yi-Lun Liao, Tess Smidt, Abhishek Das

2

15

DGFN: Double Generative Flow Networks

Elaine Lau, Nikhil Vemgal, Doina Precup, Emmanuel Bengio

2

9

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

1

10

PPI-GPT: Autoregressive Generation of Target-Specific Binding Proteins from Sequence Alone

Sophia Vincoff, Tianlai Chen, Kseniia Kholina, Shrey Goel, Pranam Chatterjee

2

2

Are Virtual Screening Methods Smarter than KNNs?

Michael Brocidiacono, Konstantin Popov, Alexander Tropsha

1

24

AlphaFold Meets Flow Matching for Generating Protein Ensembles

Bowen Jing, Bonnie Berger, Tommi Jaakkola

2

27

DiffSim for Rare Event Sampling

Martin Sipka, Johannes Dietshreit, Rafael Gómez-Bombarelli

1

15

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation

Ameya Daigavane, Song Kim, Mario Geiger, Tess Smidt

1

26

Interpretable and Automated Bias Detection for Artificial Intelligence in Healthcare

Christopher Alexiev*, Shrooq Alsenan*, Yujia Bao, Evan Rubel (* equal contribution), Regina Barzilay

1

6

MOFDiff: Coarse-grained Diffusion for Metal--Organic Framework Design

Xiang Fu, Tian Xie, Andrew Rosen, Tommi Jaakkola, Jake Smith

2

6

Integrating Machine Learning and Molecular Modeling with Directed Evolution

Azam Hussain, Charles L. Brooks III

1

1

PINDER: The protein interface dataset and resource

Mehmet Akdel, Alexander Goncearenco, Yusuf Adeshina, Daniel Kovtun, David Baugher, David Baugher, Dylan Abramson, Céline Marquet, Tomas Geffner, Zachary Carpenter, Luca Naef, Michael Bronstein

2

22

Molecular Machine Learning for Battery Electrolyte Design

Shang Zhu, Venkatasubramanian Viswanathan

2

4

Substrate Scope Contrastive Loss: Repurposing Human Bias to Learn Representations of Reactive Atoms

Wenhao Gao, Priyanka Raghavan, Ron Shprints, Connor W. Coley

2

26

Generative Marginalization Models

Sulin Liu, Peter J. Ramadge, Ryan P. Adams

2

5

MiniFold: Simple, Fast, Accurate Protein Structure Prediction

Jeremy Wohlwend, Mateo Reveiz, Axel Feldmann, Wengong Jin, Regina Barzilay

2

13

Towards Understanding Generalization for Machine Learning for Mass Spectrometry

Khoo Ling Min Serena, Peter Mikhael, Regina Barzilay

2

8

Conditional Protein Design via Doob’s h-transform

Kieran Didi, Francisco Vargas, Simon Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

1

17

Using Fragmentation Graphs for Metabolite Annotation

Yan Zhou Chen, Soha Hassoun

2

10

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters

1

29

Evaluating Molecular Graph Representations within Context

Hanchen Wang

2

16

Plausible Baselines for Molecular Conformer Generation

Eric Alcaide, Gengmo Zhou, Ziyao Li

2

20

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar

2

21