1

Variant effect prediction with reliability estimation across priority viruses

Noor Youssef, Sarah Gurev, Navami Jain, Debora Marks

Harvard Medical School, Harvard Medical School, Harvard Medical School, Harvard Medical School

AI for biology

2

Real-time Forecasting of Influenza Evolution

Aarushi Mehrotra, Navami Jain, Sarah Gurev*, Noor Youssef*, Debora Marks* (*senior authorship)

Massachusetts Institute of Technology, Harvard University, Massachusetts Institute of Technology, Harvard Medical School, Harvard Medical School

AI for biology

3

CITE V.1: Interpretable RNA-Seq Clustering with an LLM-Based Agentic Evidence-Grounded Framework

Elias Hossain, Mehrdad Shoeibi, Ivan Garibay and Niloofar Yousefi

University of Central Florida

AI for biology

4

scSPICE — Single-cell and Spatial Profile Integration for Cell state Embeddings with Large language models

Audrey Pei-Hsuan Chen, Mei-Ching Hsu, Daniol Chen

Lovemunote AI, Lovekids Clinic, Lovekids Clinic

AI for biology

5

The Dayhoff Atlas: scaling sequence diversity for improved protein generation

Kevin K. Yang, Sarah Alamdari, Alex J. Lee, Kaeli Kaymak-Loveless, Samir Char, Garyk Brixi, Carles Domingo-Enrich, Chentong Wang, Suyue Lyu, Nicolo Fusi, Neil Tenenholtz, Ava P. Amini

Microsoft

AI for biology

6

Shrinking Proteins with Diffusion

Ethan Baron, Alan Nawzad Amin, Ruben Weitzman, Debora Susan Marks, Andrew Gordon Wilson

NYU, NYU, Harvard University, Harvard University, NYU

AI for biology

7

Landscape Analysis: Volume-informed reshaping of cryo-EM latent spaces

Alkin Kaz*, Kohei Sanno*, Ellen D. Zhong

Princeton University, University of California Berkeley, Princeton University

AI for biology

8

Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra

Ziyu Xiong, Yichi Zhang, Foyez Alauddin, Chu Xin Cheng, Joon Soo An, Mohammad R. Seyedsayamdost, Ellen D Zhong

Princeton University, Princeton University, Princeton University, California Institute of Technology, Princeton University, Princeton University, Princeton University

AI for biology

9

Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry

Runzhong Wang, Mrunali Manjrekar, Babak Mahjour, Julian Avila-Pacheco, Joules Provenzano, Erin Reynolds, Magdalena Lederbauer, Eivgeni Mashin, Samuel Goldman, Mingxun Wang, Jing-Ke Weng, Desirée L. Plata, Clary B. Clish, Connor W. Coley

MIT, Broad Institute, Northeastern University, UC Riverside

AI for biology

10

Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data

Rishwanth Raghu, Axel Levy, Gordon Wetzstein, Ellen D. Zhong

Princeton University, Stanford University, Stanford University, Princeton University

AI for biology

11

CryoHype: Transformer-based hypernetworks for extreme heterogeneity in cryo-EM

Jeffrey Gu, Minkyu Jeon, Ambri Ma, Serena Yeung-Levy, Ellen Zhong

Princeton University, Princeton University, Princeton University, Stanford University, Princeton University

AI for biology

12

Separating signal from noise: a self-distillation approach for amortized heterogeneous cryo-EM reconstruction

Minkyu Jeon, Jeffrey Gu, Ambri Ma, Serena Yeung-Levy, Vincent Sitzmann, Ellen D Zhong

Princeton University, Princeton University, Princeton University, Stanford University, MIT, Princeton University

AI for biology

13

peleke-1: A Suite of Protein Language Models Fine-Tuned for Targeted Antibody Sequence Generation

Nicholas Santolla, Trey Pridgen, Prbhuv Nigam, Colby T. Ford

UNC Charlotte, UNC Charlotte, NC School of Science and Mathematics, Tuple / Silico Biosciences / UNC Charlotte

AI for biology

14

SE3Bind: SE(3)-equivariant model for antibody-antigen binding affinity prediction

Anushriya Subedy, Siddharth Bhadra-Lobo, Guillaume Lamoureux

Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08103, USA, Department of Chemistry, Rutgers University, Camden, NJ 08103, USA

AI for biology

15

Recurring antibody motifs reveal germline-encoded interactions

Fiona Qu, Sarah Gurev, Noor Youssef, Debora Marks

Harvard Medical School

AI for biology

16

Agent Driven Pipeline For Predicting Protein Function

Manvitha Ponnapati, Allan Costa, Joseph Jacobson

MIT

AI for biology

17

Molecular Docking and Dynamics Analysis of Camel Milk Peptides and Doum Fruit Flavonoids Against Mercury-Induced Oxidative Damage Targets in the Brain

Salman Haruna

Federal University Duste

AI for biology

18

RNA thermodynamics can be replaced with learnable representations

Murphy Angelo, Rose Orenbuch, Debora S. Marks

Harvard University

AI for biology

19

Improving scoring functions for protein-protein docking with LambdaLoss

Richard Zhu, Darren Xu, Lee-Shin Chu, Jeffrey J. Gray

Harvard University, Johns Hopkins University, Johns Hopkins University, Johns Hopkins University

Modeling molecular interactions

20

Real-Time Quantification and Visualization of π–π Interactions in Large Molecular Systems via the GiFE Model in BMaps

Jessica Freeze, Daniel Bryan, Matthew Newman, John Kulp Jr, Rick Bryan, John Kulp III

Conifer Point Pharmaceuticals

Modeling molecular interactions

21

Shoot from the HIP: Hessian Interatomic Potentials without derivatives

Andreas Burger, Luca Thiede, Nikolaj Rønne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik

University of Toronto, University of Toronto, Technical University of Denmark, University of Toronto, University of Toronto, Technical University of Denmark, Technical University of Denmark, University of Toronto

Modeling molecular interactions

22

Tractable Shapley Values and Interactions on Graphs via Tensor Network Surrogates

Farzaneh Heidari, Guillaume Rabusseau

UdeM, Mila

Modeling molecular interactions

23

Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Maryam Astero, Anchen Li, Elena Casiraghi, Juho Rousu

Aalto University, Aalto University, University of Milan, Aalto University & Tufts University

Modeling molecular interactions

24

Adaptive Transition State Refinement with Learned Equilibrium Flows

Samir Darouich, Vinh Tong, Tanja Bien, Johannes Kaestner, Mathias Niepert

University of Stuttgart

Modeling molecular interactions

25

Teaching Flow Models to Leap: A Simple Curriculum for Accelerating Molecule Conformer Generation

Jiwoong Park, Wengong Jin

Northeastern University

Molecular ML for drug discovery

26

ML-based Conformational Ensembles Improve Macrocyclic Peptide Permeability Oracles

Darcy Davidson, Ameya Daigavane, Emma Willet, Colin Grambow, Joseph Kleinhenz, Andrew Watkins, Bodhi P. Vani

Prescient Design at Genentech, Massachusetts Institute of Technology

Molecular ML for drug discovery

27

Diffusion-based, property-constrained molecular generation using latent space constraint mapping

Deepak Subramanian, Rowan Honeywell, Sophia Yao, Paridhi Latawa, Giovanni Traverso

MIT, Brigham and Woman's Hospital & Harvard Medical School, MIT, MIT, MIT

Molecular ML for drug discovery

28

LNPDB: structure-function database of lipid nanoparticles to advance data-driven design for nucleic acid delivery

Evan Collins, Jungyong Ji, Sung-Gwang Kim, Jacob Witten, Seonghoon Kim, Richard Zhu, Peter Park, Minjun Jung, Aron Park, Rajith S. Manan, Arnab Rudra, William J. Jeang, Wonpil Im, Robert Langer, Daniel G. Anderson

MIT, MolCube Inc., MolCube Inc., MIT, MolCube Inc., MIT, Lehigh University, MolCube Inc., MolCube Inc., MIT, MIT, MIT, Lehigh University, MIT, MIT

Molecular ML for drug discovery

29

Learning Relative Efficacy of Abiotic Peptides via Margin Ranking Loss on Cheminformatic Representations

Amirabbas Kazeminia, Nathan Dow, Andrei Loas, Bradley Pentelute

MIT

Molecular ML for drug discovery

30

MOL-SGCL: Molecular Substructure-Guided Contrastive Learning for Out-of-Distribution Generalization

Andrew Zhou,Yasha Ektefaie,Maha Farhat

Harvard Medical School, Broad Institute, Harvard Medical School

Molecular ML for drug discovery

31

Interpreting GFlowNets for Drug Discovery: Extracting Actionable Insights for Medicinal Chemistry

Amirtha Varshini A S, Duminda Ranasinghe, Hok Hei Tam

Montai Therapeutics

Molecular ML for drug discovery

32

OMTRA: A Multi-Task Generative Model for Structure-Based Drug Design

Ian Dunn, Liv Toft, Tyler Katz, Juhi Gupta, Riya Shah, Ramith Hettiarachchi, David R. Koes¬

University of Pittsburgh, Carnegie Mellon University

Molecular ML for drug discovery

33

MoleculemindFM-OM : A Generative Flow--Matching Model with Onsager--Machlup Action Paths for GPCR-ligand complexes

Hari K Prakash, Pius S Padayatti

University of California San Diego, MoleculeMindLLC

Molecular ML for drug discovery

34

Leveraging AI and structural mass spectrometry for discovery of a transient druggable pocket in TNFα

Nitzan Simchi, Gali Arad, Anjana Shenoy, Dimitri Kovalerchik, Joseph Rinberg, Alon Shtrikman, Kirill Pevzner, Eran Seger

Protai Bio, Ramat Gan, Israel

Molecular ML for drug discovery

35

Dynamics-Aware Equivariant Graph Networks for Mutation-Conditioned Allosteric Pocket Discovery in Resistant EGFR Mutations

Göknur Arıcan, Ceren Akaydın, Pınar Tunçil, İremnur Yalçın, Hanife Pekel, Özge Şensoy, Nihal Karakaş

Computer Engineering, MEF University, Istanbul, Turkey, Faculty of Medicine, Istanbul University, Istanbul, Turkey, International School of Medicine, Istanbul Medipol University, Istanbul, Turkey, Biomedical Engineering, Istanbul Medipol University, Istanbul, Turkey, School of Pharmacy, Istanbul Medipol University, Istanbul, Turkey, Faculty of Engineering and Natural Science, Istanbul Medipol University, Istanbul, Turkey, School of Medicine, Istanbul Medipol University, Istanbul, Turkey

Molecular ML for drug discovery

36

Committor Learning with Strategic Sampling Unlocks Transient Binding Sites in GPCRs

Harry Kabodha, Ayman Khaleq

Columbia University (Fu Foundation School of Engineering and Applied Sciences), Varosync

Molecular ML for drug discovery

37

State Couplings: Learning Binding Affinity through Atomic-Level Comparisons

Ryan Wong, Arvind Ramanathan, Abhishek Pandey, Chetan Rupakheti, Risi Kondor

University of Chicago, Argonne National Laboratory, Abbvie, Abbvie, University of Chicago

Molecular ML for drug discovery

38

TubercuProbe: A Cross-Attention Graph–Sequence Model for Prioritizing Compound Probes in Mycobacterium tuberculosis

Abhiram Chalamalasetty (1), Adesh Rohan Mishra (2), Fleur M. Ferguson (3,1), Benjamin Sanchez-Lengeling (4,5,6), Nathan Tran(3), José Manuel Barraza-Chavez (6,5), Adrian Jinich (3,1)

1 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego 2 Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego 3 Department of Biochemistry and Molecular Biophysics, University of California San Diego United States, 4 Department of Chemistry, University of Toronto, Toronto, Canada, 5 Vector Institute for Artificial Intelligence 6 Department of Chemical Engineering and Applied Chemistry, University of Toronto

Molecular ML for drug discovery

39

Learning Inter-Atomic Potentials without Explicit Equivariance

Ahmed A. A. Elhag*, Arun Raja*, Alex Morehead*, Samuel M Blau, Garrett M Morris, Michael M. Bronstein *denotes equal contribution

University of Oxford, LBNL

Molecular ML for drug discovery

40

Accelerating Protein Molecular Dynamics Simulation with DeepJump

Allan dos Santos Costa, Manvitha Ponnapati, Dana Rubin, Tess Smidt, Joseph Jacboson

MIT Center for Bits and Atoms, MIT Center for Bits and Atoms, MIT Center for Bits and Atoms, MIT Atomic Architects, MIT Center for Bits and Atoms

Prediction and design of biomolecular 3D structures

41

Triangle Multiplication is All You Need for Biomolecular Structure Representations

Jeffrey Ouyang-Zhang, Pranav M. Murugan, Daniel J. Diaz, Gianluca Scarpellini, Richard S. Bowen, Nate Gruver, Adam Klivans, Philipp Krähenbühl, Aleksandra Faust and Maruan Al-Shedivat

UT Austin +Genesis Research, Genesis Research, UT Austin, Genesis Research, Genesis Research, Genesis Research, UT Austin, UT Austin, Genesis Research, Genesis Research

Prediction and design of biomolecular 3D structures

42

PairSAE: Mechanistic Interpretability from Pair Representations in Protein Co-Folding

Giosue Migliorini, Aristofanis Rontogiannis, Grigori Guitchounts, Nicholas Franklin, Axel Elaldi, Olivia Viessmann

University of California Irvine, Flagship Pioneering, Flagship Pioneering, Flagship Pioneering, Flagship Pioneering, Flagship Pioneering

Prediction and design of biomolecular 3D structures

43

On fine-tuning Boltz-2 for protein-protein affinity prediction

James King, Lewis Cornwall, Andrei Christian Nica, James Day, Aaron Sim, Neil Dalchau, Lilly Wollman, Joshua Meyers

Synteny

Prediction and design of biomolecular 3D structures

44

Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

Siyuan Chen*, Minghao Guo*, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen

University of British Columbia, MIT, NVIDIA, Peking University, Foundry Biosciences, University of Utah

Prediction and design of biomolecular 3D structures

45

DriftLite: Lightweight Drift Control for Inference-Time Scaling of Co-folding Models

Yinuo Ren;Wenhao Gao;Lexing Ying;Grant Rotskoff;JIequn Han

Stanford University, Flatiron Institute

Prediction and design of biomolecular 3D structures

46

Boltz-2 for Structure-Based Discovery of EGFR Allosteric Binders

Violeta Stojalnikova, Shreya Jaiswal

Mass General Cancer Center/Harvard Medical School, Northeastern University

Prediction and design of biomolecular 3D structures

47

MORPH·EUS – Towards folding with 1,823 non-canonical amino acids

Waldherr A., Freimann G. L., Lederbauer M., Gade P.

MPI for Biology, MPI for Biology, MIT, Conjecture

Prediction and design of biomolecular 3D structures

48

Relaxed Sequence Sampling for Diverse Protein Design

Joohwan Ko,Aristofanis Rontogiannis,Yih-En Andrew Ban,Axel Elaldi,Nicholas Franklin

University of Massachusetts Amherst, Flagship Pioneering

Prediction and design of biomolecular 3D structures

49

Enhancing RFDiffusion with Resampling for de novo Design of Chondroitinase ABC Lyase I

Matthew Noyes, Robin Walters

Northeastern University, Northeastern University

Prediction and design of biomolecular 3D structures

50

Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment

Ameya Daigavane, YuQing Xie, Bodhi P. Vani, Saeed Saremi, Joseph Kleinhenz, Tess Smidt

MIT, MIT, Genentech, Genentech, Genentech, MIT

Prediction and design of biomolecular 3D structures

51

Platonic Transformers: A Solid Choice for Equivariance

Mohammad Mohaiminul Islam 1, Rishabh Anand 2, David R. Wessels 4, Friso de Kruiff 4, Thijs P. Kuipers 3, Rex Ying 2, Clara I. Sánchez 1, 3, Sharvaree Vadgama 4, Georg Bökman 4, Erik J. Bekkers 4

1 QurAI, Univ. of Amsterdam 2 Yale University, USA 3 BMEP, Amsterdam UMC 4 AMLab, Univ. of Amsterdam

Prediction and design of biomolecular 3D structures

52

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling

Mouyang Cheng, Weiliang Luo, Hao Tang, Bowen Yu, Yongqiang Cheng, Weiwei Xie, Ju Li, Heather J. Kulik, Mingda Li

MIT, MIT, MIT, MIT, Oak Ridge National Laboratory, Michigan State University, MIT, MIT, MIT

ML for quantum and materials chemistry

53

Graph Neural Networks for Melting Temperature Prediction of Molten Salt Eutectic Mixtures

Nila Mandal, James Maniscalco, Mark Aindow, Qian Yang

University of Connecticut

ML for quantum and materials chemistry

54

Learning Materials Interatomic Potentials via Hybrid Invariant-Equivariant Architectures

Keqiang Yan, Montgomery Bohde, Andrii Kryvenko, Ziyu Xiang, Kaiji Zhao, Siya Zhu, Saagar Kolachina, Doğuhan Sarıtürk, Jianwen Xie, Raymundo Arróyave, Xiaoning Qian, Xiaofeng Qian, Shuiwang Ji

Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Lambda Inc., Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University

ML for quantum and materials chemistry

55

Multi-Fidelity Bayesian Optimization of Quantum Chemistry Models for Solvent Design in a Menschutkin Reaction

Mathias Neufang, Ye Seol Lee, Claire Adjiman, Antonio del Rio Chanona

Imperial College London, University College London, Imperial College London, Imperial College London

ML for quantum and materials chemistry

56

MMomentA: Multipole Moment-based Charge Assignment for Fast, Transferable Polarizable Force Fields

Shehan Parmar, Jesse McDaniel

Georgia Institute of Technology

ML for quantum and materials chemistry

57

New targeted therapies for the treatment of X-linked diseases

Abrham Getachew Alemu1,2, Nabilla Noor1,2, Jonathan Fiorentino2,3, Gian Tartaglia2,3, and Andrea Cerase1,2,4

1. Unit of Cell and Developmental Biology, Department of Biology, Università di Pisa 2. CERNAIS LTD 3. RNA Systems Biology Lab, Center for Human Technologies, Istituto Italiano di Tecnologia 4. Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London

ML for quantum and materials chemistry

58

SNAC-DB: The Hitchhiker’s Guide to Building Better Predictive Models of Antibody & NANOBODY® VHH–Antigen Complexes

Abhinav Gupta, Bryan Munoz Rivero, Jorge Roel Tourls, Ruijiang Li, Norbert Furtmann, Yves Fomekong Nanfack, Maria Wendt, Yu Qiu

Sanofi

Molecular ML for drug discovery