MOTIFROLE-DIFF: Risk-Optimal Role-Aware Corruption for Masked Molecular Graph Diffusion

Tasfia Nuzhat Ornee, Elias Hossain, Niloofar Yousefi

University of Central Florida

Generative Al for biology

Blitz: Accelerating the Diffusion Module of AlphaFold 3 and Boltz-2

Vinay Saji Mathew, Soundar R. Kumara, William K.M. Lai

Pennsylvania State University, Pennsylvania State University, University at Buffalo

Generative Al for biology

Automated de novo structure elucidation from NMR spectra with generative models

Ziyu Xiong, Joseph Clark,R obert Heeter,Keith Matanachai, Vanessa Y. Ying,Joon Soo An, Jonathan Z. Huang, Zane Koch, Andrew D. White, Mohammad R. Seyedsayamdost, Ellen D. Zhong

Princeton University, Edison Scientific

Generative Al for biology

Escape Cost as a Target-Selection Criterion for De Novo Epitope-Scaffold Immunogen Design

Celia W. Ayad, Victoria Walker-Sperling, Dan H. Barouch

CVVR, BIDMC, Harvard Medical School

Generative Al for biology

Action Identifiability in Protein-Design Agents

Saanvi Skanda Subramanian, Gabriela Lobinska

California Institute of Technology, AITHYRA

Generative Al for biology

CellVELA: Functional Adaptation of Cell Foundation Models for Cancer Vulnerability Discovery

Jiayi Li, James J. Morrow, and Bradley E. Bernstein

Harvard University, Broad Institute of MIT and Harvard, Dana-Farber Cancer Institute

Generative Al for biology

Co-folding model guided by structural proteomics

Alon Shtrikman, Sagie Brodsky, Michal Ran Shchory, Nitzan Simchi, Yaron Ben Shoshan-Galeczki, Eran Seger, Kirill Pevzner

Protai

Generative Al for biology

Continuous Variational Synthesis

Alan Nawzad Amin, Mattia G Gollub, Andrei Slabodkin, Elizabeth Baker Wood, Eli N Weinstein

JURA bio, Denmark Technical University

Generative Al for biology

Latent Diffusion Pretraining for Material Property Prediction

Kishalay Das

Yale University

ML for quantum and materials chemistry

Hamiltonian Normalizing Flows for Equilibrium Sampling of Simple Particle Systems

Adam Tobin-Williams, Holden Lee, David Rogers

MIT, Johns Hopkins University, Oak Ridge National Laboratory

ML for quantum and materials chemistry

Predicting Adjacent-Lanthanide Selectivity for New Extractants from the Solvent-Extraction Record

Bogdan Mironov, Dias Daulet, Konstantinos D. Vogiatzis

Berea College, NIS Nauryzbay, University of Tennessee Knoxville

ML for quantum and materials chemistry

Fermionic Shadow Regression: Amortized Learning of Molecular Electron Dynamics

Aniket Deshpande, Karthik Panicker, Luis Mantilla Calderon, Mohsen Bagherimehrab, Alán Aspuru-Guzik

University of Illinois Urbana-Champaign, University of Toronto, Vector Institute for Artificial Intelligence, NVIDIA, CIFAR

ML for quantum and materials chemistry

Gaussian Splatting for Density Functional Theory

Andres Guzman Cordero, Cindy Zhang, Majdi Hassan, Marta Skreta, Matija Medvidovic, Kirill Neklyudov

Mila - Quebec AI Institute and Université de Montréal, Princeton University, Mila - Quebec AI Institute and Université de Montréal, Mila - Quebec AI Institute and Université de Montréal, ETH Zurich, Mila - Quebec AI Institute and Université de Montréal and Instituut Courtois

ML for quantum and materials chemistry

BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials

Laura Zichi, Gil Harari, Chuin Wei Tan, Albert Zhu, Marc L. Descoteaux, Menghang Wang, H.T. Kung, Boris Kozinsky

Harvard University, Robert Bosch LLC Research and Technology Center

Modeling molecular interactions

EqFSA: SE(3)-Equivariant Fourier Space Attention

Adithya Rao, Xu Liang

Independent, City University of Hong Kong

Modeling molecular interactions

Fine-tuning Boltz-1 for protein-protein interaction prediction with positive and negative data

Ruqi Liao, Sarah Gurev, Ashley Gin, Yo Akiyama, James Wells, Greta Pintacuda, Sergey Ovchinnikov*, Hilary Finucane*

Broad Institute, MIT, MIT, MIT, UCSF, Broad Institute, MIT, Broad Institute

Modeling molecular interactions

Critical Mass: Structure-Disjoint Splits Stop Measuring Structure as Libraries Grow

Tomo Oga, Devesh Shah, Cailum M. K. Stienstra, Gabriel Asher, Antonio H. de O. Fonseca, Niall O'Connor, Michael Widrich

Northeastern University, Matterworks Inc.

Molecular ML for drug discovery

Toxicity Prediction Tools Do Not Generalize to Novel Chemistry

Circe Hsu, Martin Weiss, and Antonio H. de O. Fonseca

Matterworks, Inc.

Molecular ML for drug discovery

zPocket: Finding Pockets with Frozen Cofolding Features

Tally Portnoi, Allison M. Keys, Benjamin R. DiFrancesco, Nate Gruver

MIT, Genesis Therapeutics

Molecular ML for drug discovery

Calibrated Blood-Brain Barrier Screening with Conformal Prediction: When Mondrian Helps and When It Does Not

Sanjeda Sara Jennifer, Niloofar Yousefi

University of Central Florida

Molecular ML for drug discovery

FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space

Sihyun Park, David Ryan Koes

Carnegie Mellon University, University of Pittsburgh

Molecular ML for drug discovery

MolSAGE: Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery

Yikun Zhang, Xiwei Cheng, Tianyu Liu, Yuanqi Du, Wengong Jin

Northestern Univerisity, Broad Institute of MIT and Harvard, Yale University, Microsoft Research New England

Molecular ML for drug discovery

Synthesizability-Guided Discrete Diffusion Model for Molecular Generation

Haokun Zhao, Sharvaree Vadgama, Kunhuan Liu, Michael K. Gilson, Rose Yu

UC San Diego

Molecular ML for drug discovery

MIRAGE: Measuring Interpolation and Redundancy in Affinity GEneralization

Mehdi Yazdani-Jahromi, Sanjay Padhi, Ivan Garibay

DeepBio Scientific

Molecular ML for drug discovery

Objective Discovery to Model Optimization: From Objective Discovery to Model Optimization:A Self-Evolving Molecular Intelligence Loop

Yikun Zhang, Xiwei Cheng, Tianyu Liu, Yuanqi Du, Wengong Jin

Northeastern University, Broad Institute, Yale University, Microsoft Research New England

Molecular ML for drug discovery

Synthesizability in drug design: towards a unified evaluation framework

Marios Gavrielatos, David R. Koes

University of Pittsburgh

Molecular ML for drug discovery

Retrosynthetic Accessibility as a Guidance for Masked Discrete Diffusion

Haokun Zhao, Sharvaree Vadgama, Kunhuan Liu, Michael K. Gilson, Rose Yu

University of California, San Diego

Molecular ML for drug discovery

Breaking the Memory Barrier for NNP-Molecular Dynamic

Huanyi Qin, Shehtab Zaman, Obadiah Smolenski, Preston Kostiukov, Robin Cramer, Hiten Malhotra, Jayakaran Saravanan Indira, Brian Van Essen , Kenneth Chiu

Binghamton University, Memorial Sloan Kettering Cancer Center, Binghamton University, Binghamton University, Binghamton University, Binghamton University, Binghamton University, Lawrence Livermore National Laboratory, Binghamton University

Molecular dynamics

Transferable Free-Energy Priors for MLIP-Driven Sampling of CO2 Chemisorption in Isoreticular Covalent-Organic Frameworks

Bogdan Mironov, Artur Lyssenko, Sauradeep Majumdar, Rafael Gómez-Bombarelli

Berea College, MIT/Harvard University, MIT, MIT

Molecular dynamics

Rapid Generation of Antibody Conformational Ensembles Using Machine Learning

Pranav V. Rao, David Sommer, Darcy Davidson, Robert G. Alberstein, Jan Ludwiczak, Joseph Kleinhenz, Bodhi Vani, Eliott Park, Jae Hyeon Lee, Saeed Izadi, James R. Kiefer, Richard Bonneau, Sai Pooja Mahajan, Andrew M. Watkins, Frédéric A. Dreyer

Prescient Design, Genentech, South San Francisco, CA, USA; Pharmaceutical Development, Genentech, South San Francisco, CA, USA; Department of Structural Biology, Genentech, South San Francisco, CA, USA

Molecular dynamics

From 13 Hours to 4.6: Profiling-Driven Kernel Fusion for TensorNet in Molecular Simulation

Manpreet Singh

Thapar Institute of Engineering & Technology, Embedded LLM

Molecular dynamics

Learning DNA origami mechanics in extensional flow from a mechanistic model

Richard B. Huang, Patrick S. Doyle

MIT

Molecular dynamics

Controlled AlphaFold3 Evaluation of QTY-Redesigned Claudin-4 and Claudin-9: Fold Preservation with Reduced Hydrophobicity

David Guo, William Guo

Independent

Molecule/Protein 3D structure prediction and processing

What ipTM Misses: Target-Conditioned Functional Signal in ESM-C’s Internal Representations

Vignesh Karthik, Zuliayeti Abudujielili, Junming Qian, Zhaojun Wang, Carlotta Benthin, Shawn Liu

Columbia University

Molecule/Protein 3D structure prediction and processing

Closing the sim2real gap in cryo-EM with a biophysically-grounded density map simulator

Ariana-Dalia Vlad, Ellen D. Zhong

Princeton University

Molecule/Protein 3D structure prediction and processing

spinet: Sheaf Protein Inverse Folding Network

Jens Lundsgaard, Colin Mikulski, Zhixuan Yan, Dhananjay Bhaskar

University of Wisconsin-Madison

Molecule/Protein 3D structure prediction and processing

Dissecting the limitations of AlphaFold3 confidence metrics for de novo antibody design selection

Minjae Park, Brian Pierce

University of Maryland College Park & University of Maryland Institute for Bioscience and Biotechnology Research

Molecule/Protein 3D structure prediction and processing

Hyaline: Kinase-State Prediction Can Learn Taxonomy Instead

Ayman Khaleq, Harry Kabodha, Manju Selvakumaran, Sasha Kakkassery

Northeastern University, Varosync Foundation

Molecule/Protein 3D structure prediction and processing

Structure-based antibody renumbering

Diego del Alamo

Takeda

Molecule/Protein 3D structure prediction and processing

Electro-Prot: Learning Protein Representations from Electrostatics

Paul Sharoubeem, Sumeet Kothare, Jacky Chen, David R. Koes

University of Pittsburgh, Carnegie Mellon University

Molecule/Protein 3D structure prediction and processing

Sampling Fold-Switching Transitions with ProteinEBM

Gabriel Au, James P. Roney, Sergey Ovchinnikov

MIT

Molecule/Protein 3D structure prediction and processing

Free Energy Estimation on Any State Space

Jiajun He, Zijing Ou, Francisco Vargas, Yingzhen Li, José Miguel Hernández-Lobato, Carles Domingo-Enrich, Yuanqi Du

University of Cambridge, Imperial College London, Xaira Therapeutics, Microsoft Research New England

Other

Steering GFlowNets for Drug Discovery: Which Directions Control Molecule Generation

Amirtha Varshini A S, Xinmeng Li, Matthew Parks, Hok Hei Tam

Montai Therapeutics

Other