2023 Accepted Papers

Paper Title	Paper Authors
Role of Structural and Conformational Diversity for Machine Learning Potentials	Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
datamol.io - an open-source ecosystem of tools for AI-based drug discovery	Hadrien Mary, Emmanuel Noutahi, Cas Wognum, Michael Craig, Lu Zhu
Gotta be SAFE: A New Framework for Molecular Design	Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S.C Lim, Prudencio Tossou
The Discovery of Binding Modes Requires Rethinking Docking Generalization	Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi Jaakkola
Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms	Bowen Jing, Tommi Jaakkola, Bonnie Berger
AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2	Artem Gazizov, Anna Lian, Casper Goverde, Sergey Ovchinnikov, Nicholas Polizzi
Automated segmentation of cryo-electron tomograms	Rishwanth Raghu, S. Mazdak Abulnaga, Mahrukh Usmani, Nicolas Coudray, Gira Bhabha, Damian Ekiert, Ellen Zhong
PepPrCLIP: De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone	Suhaas Bhat, Kalyan Palepu, Pranam Chatterjee
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
Transition Path Sampling with Boltzmann Generator-based MCMC Moves	Michael Plainer, Hannes Stark, Charlotte Bunne, Stephan Gunnemann
A benchmark of dynamic molecular complexes for cryo-EM structure determination	Minkyu Jeon, Alkin Kaz, Rishwanth Raghu, Ellen D. Zhong
STRIDE: Structure-guided Generation for Inverse Design of Molecules	Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo, Kenneth Chiu
G2Retro as a two-step graph generative models for retrosynthesis prediction	Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, Xia Ning
PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling	Tianlai Chen, Sarah Pertsemlidis, Pranam Chatterjee
Accessible Molecular Machine Learning Models for the Future of Drug Discovery	Jessica G. Freeze
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
Data-Centric Learning from Unlabeled Graphs with Diffusion Model	Gang Liu, Meng Jiang
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
A graph representation of molecular ensembles for polymer property prediction	Matteo Aldeghi, Connor W. Coley
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
ML Driven Photochemical Synthesis of Helicenes	Lucia Vina-Lopez, Johannes Dietschreit, Simon Axelrod, Aik Rui Tan, Vikas Vashney, Rafael Gomez-Bombarelli
A Robust, Scalable, and Versatile Approach to Ab Initio Heterogeneous Reconstruction with CryoDRGN-AI	Axel Levy, Frédéric Poitevin, Gordon Wetzstein, Ellen D Zhong
Triangular Contrastive Learning on Molecular Graphs	MinGyu Choi, Wonseok Shin, Yijingxiu Lu, Sun Kim
Local, Learned Frames for Molecules	Hannah Lawrence, Saro Passaro, Abhishek Das
DSMBind: an unsupervised generative modeling framework for binding energy prediction	Wengong Jin, Xun Chen, Amrita Vetticaden, Raktima Raychowdhury,Siranush Sarzikova, Caroline Uhler, Nir Hacohen
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size	Albert Musaelian, Anders Johansson, Simon Batzner, Boris Kosinsky
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion	Frazier N. Baker, Ziqi Chen, Xia Ning
Mapping the intermolecular interaction universe through self-supervised learning on molecular crystals	Ada Fang, Zaixi Zhang, Marinka Zitnik
Machine Learning for Molecules: A Comparative Study of Style Transfer Models	Vedika Srivastava, Hemant Kumar Singh
Multi-Fidelity Active Learning with GFlowNets	Alex Hernandez-Garcia, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio
Molecular transmutation with hierarchically branched diffusion models	Alex Tseng, Tommaso Biancalani, Gabriele Scalia
Graph-Based Prediction of Biocatalyzed Reactions	Peter G. Mikhael, Itamar Chinn, Regina Barzilay
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design	Hannes Stark, Bowen Jing, Regina Barzilay, Tommi Jaakkola
Learning Interatomic Potentials at Multiple Scales	Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky
Protein generation with evolutionary diffusion	Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X. Lu, Nicolo Fusi, Ava P. Amini, Kevin K. Yang
Structure-Infused Protein Language Models	Daniel Penaherrera, David Ryan Koes
Removing Biases from Molecular Representations via Information Maximization	Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola
Improving Graph Generation by Restricting Graph Bandwidth	Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields	Yi-Lun Liao, Tess Smidt, Abhishek Das
DGFN: Double Generative Flow Networks	Elaine Lau, Nikhil Vemgal, Doina Precup, Emmanuel Bengio
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization	Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio
PPI-GPT: Autoregressive Generation of Target-Specific Binding Proteins from Sequence Alone	Sophia Vincoff, Tianlai Chen, Kseniia Kholina, Shrey Goel, Pranam Chatterjee
Are Virtual Screening Methods Smarter than KNNs?	Michael Brocidiacono, Konstantin Popov, Alexander Tropsha
AlphaFold Meets Flow Matching for Generating Protein Ensembles	Bowen Jing, Bonnie Berger, Tommi Jaakkola
DiffSim for Rare Event Sampling	Martin Sipka, Johannes Dietshreit, Rafael Gómez-Bombarelli
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation	Ameya Daigavane, Song Kim, Mario Geiger, Tess Smidt
Interpretable and Automated Bias Detection for Artificial Intelligence in Healthcare	Christopher Alexiev, Shrooq Alsenan, Yujia Bao, Evan Rubel (* equal contribution), Regina Barzilay
MOFDiff: Coarse-grained Diffusion for Metal--Organic Framework Design	Xiang Fu, Tian Xie, Andrew Rosen, Tommi Jaakkola, Jake Smith
Integrating Machine Learning and Molecular Modeling with Directed Evolution	Azam Hussain, Charles L. Brooks III
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
Molecular Machine Learning for Battery Electrolyte Design	Shang Zhu, Venkatasubramanian Viswanathan
Substrate Scope Contrastive Loss: Repurposing Human Bias to Learn Representations of Reactive Atoms	Wenhao Gao, Priyanka Raghavan, Ron Shprints, Connor W. Coley
Generative Marginalization Models	Sulin Liu, Peter J. Ramadge, Ryan P. Adams
MiniFold: Simple, Fast, Accurate Protein Structure Prediction	Jeremy Wohlwend, Mateo Reveiz, Axel Feldmann, Wengong Jin, Regina Barzilay
Towards Understanding Generalization for Machine Learning for Mass Spectrometry	Khoo Ling Min Serena, Peter Mikhael, Regina Barzilay
Conditional Protein Design via Doob’s h-transform	Kieran Didi, Francisco Vargas, Simon Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
Using Fragmentation Graphs for Metabolite Annotation	Yan Zhou Chen, Soha Hassoun
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
Evaluating Molecular Graph Representations within Context	Hanchen Wang
Plausible Baselines for Molecular Conformer Generation	Eric Alcaide, Gengmo Zhou, Ziyao Li
Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning	Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar

Accepted Posters

The following table displays the accepted posters and which poster session they will be part of. We will announce accepted posters on Oct. 18, 2023.

Poster Format

Posters should be sized 48” x 36”, and oriented in landscape.
All posters should be physically printed. No screens will be available for electronic posters.
Posters should be printed and brought to the conference on November 8, 2023. Please do not mount your poster on foam core. We will provide push pins for you to affix your poster to the poster board.

Poster Session

Location. The poster session will take place on the 1st floor of the Koch Institute in the main hallway and in Luria Auditorium.
Schedule and Map. Board numbers and board assignments will be provided day-of at the Registration Table.

Last updated: Aug. 10, 2023