Valence Labs

BindCraft: one-shot design of functional protein binders

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold | Lazar Atanackovic

Derivative-Free Guidance in Continuous and Discrete Diffusion Models | Xiner Li and Masatoshi Uehara

A long-context RNA foundation model for predicting transcriptome architecture | Ali Saberi

Fine-tuning Flow and Diffusion Generative Models | Carles Domingo-Enrich

Probabilistic Inference in Language Models via Twisted Sequential Monte | Rob Brekelmans

Geometric deep learning framework for de novo genome assembly | Lovro Vrček

An Open MetaGenomic corpus for mixed-modality genomic language modeling | Andre Cornman

Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure | Amy X. Lu

Discrete Flow Matching | Andrew Campbell

Gene regulatory network structure informs the distribution of perturbation effects | Matthew Aguirre

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion | Boyuan Chen

On the Future of AI for Drug Discovery | Yoshua Bengio, Marinka Zitnik, Oren Kraus, and more

LOWE: An LLM-Orchestrated Workflow Engine for the Recursion OS

Transformers Meet Directed Graphs | Simon Geisler

Applying Active Learning in Drug Discovery | Pat Walters & James Thompson

Data-Efficient Graph Grammar Learning for Molecular Generation - Minghao Guo

Generative Language Models in Molecular Discovery: Regression Transformer, GT4SD and Beyond

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Rigid Body Flows for Sampling Molecular Crystal Structures | Jonas Köhler

Benchmark and Critical Evaluation for ML Force Fields with Molecular Simulations | Xiang Fu

MoML 2023: Opening Remarks | Yoshua Bengio & Dominique Beaini

Molecular Machine Learning at Scale: Lessons from the OGB Large Scale Challenge | Graphcore

Learning Equivariant Non-Local Electron Density Functionals | Nicholas Gao

Automatic Symmetry Discovery with Lie Algebra Convolutional Network | Nima Dehmamy

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

Chemically Transferable Generative Backmapping of Coarse-Grained Proteins | Soojung Yang

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

Recap: Valence Labs ICML 2023 Launch

The Statistical Structure of Identifiable Generative Models | Johnny Xi

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling

Uncovering and Inducing Interpretable Causal Structure in Deep Learning Models | Atticus Geiger

Structure-Aware Protein Self-Supervised Learning | Can (Sam) Chen

Ewald-Based Long-Range Message Passing for Molecular Graphs | Arthur Kosmala

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

Graph Neural Networks for Link Prediction with Subgraph Sketching

Machine Learning Molecules | Gianni De Fabritiis

Learning Quantum Mechanics Using Embedding, Symmetric Polynomials and Composition | Gábor Csányi