Structure-Independent Peptide Binder Design via Generative Language Models | Pranam Chatterjee

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

Training Neural Network Potentials: Bayesian and Simulation-based Approaches | Stephan Thaler

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling | Soumendranath Bhakat

Machine Learning Molecules | Gianni De Fabritiis

Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

There’s no free lunch, but you can get a discount – applying active learning in drug discovery | Pat Walters & James Thompson

Cell Morphology-Guided De Novo Hit Design by Conditioning GANs on Phenotypic Image Features | Paula A. Marin Zapata

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes | William McCorkindale

Calibration and Generalizability of Probabilistic Models on Low-Data Chemical Datasets With DIONYSUS | Gary Tom

Interpretable Chirality-Aware GNNs for QSAR Modeling in Drug Discovery | Yunchao (Lance) Liu

Structure-aware Protein Self-supervised Learning | Can Chen

Differentiable Simulations for Enhanced Sampling of Rare Events | Rafael Gomez-Bombarelli

Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction | Zhen Liu

Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description | Shengchao Liu

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations | Xiang Fu

Artificial Chemical Intelligence: AI for Chemistry and Chemistry for AI | Pratyush Tiwary

ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on Molecular Dynamics Simulations | Fang Wu