Канал: Valence Labs

Scalable molecular simulation of electrolyte solutions with quantum chemical accuracy | Tim Duignan

Scalable molecular simulation of electrolyte solutions with quantum chemical accuracy | Tim Duignan

MoML 2024

MoML 2024

Approximately Piecewise E(3) Equivariant Point Networks | Matan Atzmon

Approximately Piecewise E(3) Equivariant Point Networks | Matan Atzmon

gRNAde: Geometric Deep Learning for 3D RNA inverse design | Chaitanya K. Joshi

gRNAde: Geometric Deep Learning for 3D RNA inverse design | Chaitanya K. Joshi

Contextual AI Models for Single-cell Protein Biology | Michelle Li

Contextual AI Models for Single-cell Protein Biology | Michelle Li

Identifying Representations for Intervention Extrapolation | Sorawit (James) Saengkyongam

Identifying Representations for Intervention Extrapolation | Sorawit (James) Saengkyongam

The Continuous Language of Protein Structure | Ben Murrell

The Continuous Language of Protein Structure | Ben Murrell

The Future of Chemistry is Self-Driving | Alán Aspuru-Guzik

The Future of Chemistry is Self-Driving | Alán Aspuru-Guzik

Weisfeiler Leman for Euclidean Equivariant Machine Learning | Snir Hordan

Weisfeiler Leman for Euclidean Equivariant Machine Learning | Snir Hordan

A Causal Inference Framework for Combinatorial Interventions | Anish Agarwal

A Causal Inference Framework for Combinatorial Interventions | Anish Agarwal

KAN: Kolmogorov-Arnold Networks | Ziming Liu

KAN: Kolmogorov-Arnold Networks | Ziming Liu

D-Flow: Differentiating through Flows for Controlled Generation | Heli Ben-Hamu

D-Flow: Differentiating through Flows for Controlled Generation | Heli Ben-Hamu

EquiReact: An Equivariant Neural Network for Chemical Reactions | Puck van Gerwen

EquiReact: An Equivariant Neural Network for Chemical Reactions | Puck van Gerwen

Multimodal language models for mapping the genotype-phenotype relationship | Farhan Khodaee

Multimodal language models for mapping the genotype-phenotype relationship | Farhan Khodaee

Causal Abstractions using Generalized Functions | Sander Beckers

Causal Abstractions using Generalized Functions | Sander Beckers

Learning to Group Auxiliary Datasets for Molecule | Tinglin Huang

Learning to Group Auxiliary Datasets for Molecule | Tinglin Huang

Smooth, exact rotational symmetrization for deep learning on point clouds | Sergey Pozdnyakov

Smooth, exact rotational symmetrization for deep learning on point clouds | Sergey Pozdnyakov

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

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

Local Search GFlowNets | Minsu Kim

Local Search GFlowNets | Minsu Kim

Mosaic-SDF for 3D Generative Models | Lior Yariv

Mosaic-SDF for 3D Generative Models | Lior Yariv

Stability-Aware Boltzmann Estimator (StABlE) Training of NN Interatomic Potentials | Sanjeev Raja

Stability-Aware Boltzmann Estimator (StABlE) Training of NN Interatomic Potentials | Sanjeev Raja

Combinatorial perturbation prediction using causally-inspired neural networks | Guadalupe Gonzalez

Combinatorial perturbation prediction using causally-inspired neural networks | Guadalupe Gonzalez

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems | Mathis, Joshi, and Duval

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems | Mathis, Joshi, and Duval

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions | Emma Svensson

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions | Emma Svensson

Diffusion Models on Sampling Rare Events | Chenru Duan

Diffusion Models on Sampling Rare Events | Chenru Duan

MFBind: a Multi-Fidelity Approach for Evaluating Drugs in Generative Modeling | Peter Eckmann

MFBind: a Multi-Fidelity Approach for Evaluating Drugs in Generative Modeling | Peter Eckmann

Meaningful Causal Aggregation and Paradoxical Confounding | Yuchen Zhu

Meaningful Causal Aggregation and Paradoxical Confounding | Yuchen Zhu

Evaluating retrosynthesis with syntheseus and retro-fallback | Austin Tripp

Evaluating retrosynthesis with syntheseus and retro-fallback | Austin Tripp

MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation | Omer Bar-Tal

MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation | Omer Bar-Tal

Additive Decoders for Latent Variables Identification | Sébastien Lachapelle

Additive Decoders for Latent Variables Identification | Sébastien Lachapelle

Removing Biases from Molecular Representations via Information Maximization | Chenyu Wang

Removing Biases from Molecular Representations via Information Maximization | Chenyu Wang

Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms | Bowen Jing

Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms | Bowen Jing

Generalization in diffusion models from geometry-adaptive harmonic representation | Zahra Kadkhodaie

Generalization in diffusion models from geometry-adaptive harmonic representation | Zahra Kadkhodaie

Manifold Diffusion Fields | Ahmed Elhag

Manifold Diffusion Fields | Ahmed Elhag

Linear Structure of High-Level Concepts in Text-Controlled Generative Models | Victor Veitch

Linear Structure of High-Level Concepts in Text-Controlled Generative Models | Victor Veitch

AlphaFold Meets Flow Matching for Generating Protein Ensembles | Bowen Jing

AlphaFold Meets Flow Matching for Generating Protein Ensembles | Bowen Jing

Multiflow: protein structure and sequence co-generation | Jason Yim & Andrew Campbell

Multiflow: protein structure and sequence co-generation | Jason Yim & Andrew Campbell

BISCUIT: Causal Representation Learning from Binary Interactions | Phillip Lippe

BISCUIT: Causal Representation Learning from Binary Interactions | Phillip Lippe

De novo design of high-affinity binders of bioactive helical peptides | Susana Vazquez Torres

De novo design of high-affinity binders of bioactive helical peptides | Susana Vazquez Torres

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion | Jesse Lai

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion | Jesse Lai

Integrating single-cell data with substantial batch effects | Karin Hrovatin

Integrating single-cell data with substantial batch effects | Karin Hrovatin

Protein Discovery with Discrete Walk-Jump Sampling | Nathan Frey

Protein Discovery with Discrete Walk-Jump Sampling | Nathan Frey

Predictive Chemistry Augmented with Text Retrieval | Zhening Li

Predictive Chemistry Augmented with Text Retrieval | Zhening Li

Weakly supervised causal representation learning | Johann Brehmer

Weakly supervised causal representation learning | Johann Brehmer

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design | Xiang Fu

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design | Xiang Fu

Structured State-Space Sequence Models for De Novo Drug Design | Rıza Özçelik

Structured State-Space Sequence Models for De Novo Drug Design | Rıza Özçelik

Conditional Graph Information Bottleneck for Molecular Relational Learning | Namkyeong Lee

Conditional Graph Information Bottleneck for Molecular Relational Learning | Namkyeong Lee

Identifiable Deep Generative Models via Sparse Decoding | Gemma Moran

Identifiable Deep Generative Models via Sparse Decoding | Gemma Moran

Towards Foundation Models for Knowledge Graph Reasoning | Mikhail Galkin

Towards Foundation Models for Knowledge Graph Reasoning | Mikhail Galkin

Linear Causal Representations from Interventions under General Nonlinear Mixing | Goutham Rajendran

Linear Causal Representations from Interventions under General Nonlinear Mixing | Goutham Rajendran

SUREL+: From Walks to Sets for Scalable Subgraph-based Graph Representation Learning | Haoteng Yin

SUREL+: From Walks to Sets for Scalable Subgraph-based Graph Representation Learning | Haoteng Yin

ChiENN: Embracing Molecular Chirality with Graph Neural Networks | Piotr Gaiński

ChiENN: Embracing Molecular Chirality with Graph Neural Networks | Piotr Gaiński

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

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

Portal: Home of the TechBio Community

Portal: Home of the TechBio Community

Introducing SAFE: A New Framework for Molecular Design

Introducing SAFE: A New Framework for Molecular Design

SE(3)-Stochastic Flow Matching for Protein Backbone Generation | Tara Akhound-Sadegh, Joey Bose

SE(3)-Stochastic Flow Matching for Protein Backbone Generation | Tara Akhound-Sadegh, Joey Bose

Transport, Variational Inference and Diffusions | Francisco Vargas

Transport, Variational Inference and Diffusions | Francisco Vargas

PoseBusters: AI-based docking methods fail to generate physically valid poses | Martin Buttenschoen

PoseBusters: AI-based docking methods fail to generate physically valid poses | Martin Buttenschoen

Causal Representation Learning via Soft Interventions | Jiaqi Zhang

Causal Representation Learning via Soft Interventions | Jiaqi Zhang

Harnessing Geometric Signatures in Causal Representation Learning | Yixin Wang

Harnessing Geometric Signatures in Causal Representation Learning | Yixin Wang

Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models | Gabriele Corso

Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models | Gabriele Corso

A Computational Framework for Solving Wasserstein Lagrangian Flows | Kirill Neklyudov

A Computational Framework for Solving Wasserstein Lagrangian Flows | Kirill Neklyudov

Introduction to the CARE (Causality, Abstraction, Reasoning, & Extrapolation) Reading Group

Introduction to the CARE (Causality, Abstraction, Reasoning, & Extrapolation) Reading Group

The Statistical Structure of Identifiable Generative Models | Johnny Xi

The Statistical Structure of Identifiable Generative Models | Johnny Xi

Timewarp: Transferable Acceleration of MD by Time-Coarsened Dynamics | Leon Klein & Andrew Foong

Timewarp: Transferable Acceleration of MD by Time-Coarsened Dynamics | Leon Klein & Andrew Foong

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom | Rohith Krishna

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom | Rohith Krishna

Learning Representations on Biological Data with Weakly Supervised Learning | Ian Smith

Learning Representations on Biological Data with Weakly Supervised Learning | Ian Smith

Bayesian Flow Networks | Alex Graves

Bayesian Flow Networks | Alex Graves

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution | Eric Nguyen

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution | Eric Nguyen

Machine Learning for Multi-Scale Molecular Simulation and Design | Xiang Fu

Machine Learning for Multi-Scale Molecular Simulation and Design | Xiang Fu

Mirror Diffusion Models for Constrained and Watermarked Generation | Guan-Horng Liu

Mirror Diffusion Models for Constrained and Watermarked Generation | Guan-Horng Liu

Action Matching: Learning Stochastic Dynamics from Samples | Kirill Neklyudov

Action Matching: Learning Stochastic Dynamics from Samples | Kirill Neklyudov

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Designing Losses for Data-Free Training of Normalizing Flows on Boltzmann Distributions

Designing Losses for Data-Free Training of Normalizing Flows on Boltzmann Distributions

Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and More | Hannes Stark

Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and More | Hannes Stark

Equivariant flow matching | Leon Klein

Equivariant flow matching | Leon Klein

Large Language Molecular Representation and Learning | Amir Barati Farimani

Large Language Molecular Representation and Learning | Amir Barati Farimani

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions | Michael Albergo

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions | Michael Albergo

A Tutorial on Causal Representation Learning | Jason Hartford & Dhanya Sridhar

A Tutorial on Causal Representation Learning | Jason Hartford & Dhanya Sridhar

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning | Shuxin Zheng

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning | Shuxin Zheng

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models | Yilun Xu

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models | Yilun Xu

Transformers Meet Directed Graphs | Simon Geisler

Transformers Meet Directed Graphs | Simon Geisler

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport | Alex Tong

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport | Alex Tong

Real-World Molecular Out-Of-Distribution: Specification and Investigation

Real-World Molecular Out-Of-Distribution: Specification and Investigation

Recap: Valence Labs ICML 2023 Launch

Recap: Valence Labs ICML 2023 Launch

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

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

PepFlow: Direct Conformational Sampling From Peptide Energy Landscapes | Osama Abdin

PepFlow: Direct Conformational Sampling From Peptide Energy Landscapes | Osama Abdin

Clifford Group Equivariant Neural Networks | David Ruhe

Clifford Group Equivariant Neural Networks | David Ruhe

Identifiability of Causal Models and Applications to Perturb-Seq Data | Chandler Squires

Identifiability of Causal Models and Applications to Perturb-Seq Data | Chandler Squires

ICML 2023 Panel: Fostering the Development of Impactful AI Models in Drug Discovery

ICML 2023 Panel: Fostering the Development of Impactful AI Models in Drug Discovery

Topological Deep Learning: Going Beyond Graph Data | Mustafa Hajij

Topological Deep Learning: Going Beyond Graph Data | Mustafa Hajij

Improvements on GFlowNets Applied to Molecular Discovery | Emmanuel Bengio

Improvements on GFlowNets Applied to Molecular Discovery | Emmanuel Bengio

Is Distance Matrix Enough for Geometric Deep Learning? | Zian Li

Is Distance Matrix Enough for Geometric Deep Learning? | Zian Li

DRew: Dynamically Rewired Message Passing with Delay | Benjamin Gutteridge

DRew: Dynamically Rewired Message Passing with Delay | Benjamin Gutteridge

Differentiable Simulations for Enhanced Sampling of Rare Events | Martin Šípka

Differentiable Simulations for Enhanced Sampling of Rare Events | Martin Šípka

Rigid Body Flows for Sampling Molecular Crystal Structures | Jonas Köhler

Rigid Body Flows for Sampling Molecular Crystal Structures | Jonas Köhler

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

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

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

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

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

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

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

Molecular Representations for Drug Discovery | Djork-Arné Clevert

Molecular Representations for Drug Discovery | Djork-Arné Clevert

Molecular Modeling of Complex Biological Systems | Anna Panchenko

Molecular Modeling of Complex Biological Systems | Anna Panchenko

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

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

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

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

Geometric Machine Learning for Molecular Medicine | Marinka Zitnik

Geometric Machine Learning for Molecular Medicine | Marinka Zitnik

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

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

Accelerating Cryptic Pocket Discovery With Deep Learning | Greg Bowman

Accelerating Cryptic Pocket Discovery With Deep Learning | Greg Bowman

OpenFold: Lessons Learned and Insights Gained From Rebuilding and Retraining AlphaFold2

OpenFold: Lessons Learned and Insights Gained From Rebuilding and Retraining AlphaFold2

MoML 2023: Opening Remarks | Yoshua Bengio & Dominique Beaini

MoML 2023: Opening Remarks | Yoshua Bengio & Dominique Beaini

Reflected Diffusion Models | Aaron Lou

Reflected Diffusion Models | Aaron Lou

A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks

A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks

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

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

Riemannian Diffusion Models | Chin-Wei Huang and Milad Aghajohari

Riemannian Diffusion Models | Chin-Wei Huang and Milad Aghajohari

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling

Graph Neural Networks for Link Prediction with Subgraph Sketching

Graph Neural Networks for Link Prediction with Subgraph Sketching

GenPhys: From Physical Processes to Generative Models | Ziming Liu

GenPhys: From Physical Processes to Generative Models | Ziming Liu

Machine Learning Molecules | Gianni De Fabritiis

Machine Learning Molecules | Gianni De Fabritiis

Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

Pre-training via Denoising for Molecular Property Prediction | Sheheryar Zaidi

Pre-training via Denoising for Molecular Property Prediction | Sheheryar Zaidi

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

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

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

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

Cell Morphology-Guided De Novo Hit Design by Conditioning GANs on Phenotypic Image Features

Cell Morphology-Guided De Novo Hit Design by Conditioning GANs on Phenotypic Image Features

Path Integral Stochastic Optimal Control for Sampling Transition | Lars Holdijk

Path Integral Stochastic Optimal Control for Sampling Transition | Lars Holdijk

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

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

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

DGL 1.0: Empowering Graph Machine Learning for Everyone | Minjie Wang

DGL 1.0: Empowering Graph Machine Learning for Everyone | Minjie Wang

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

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

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

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

An Optimal Control Perspective on Diffusion-Based Generative Modeling | Julius Berner

An Optimal Control Perspective on Diffusion-Based Generative Modeling | Julius Berner

Valence Discovery Live Stream

Valence Discovery Live Stream

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

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

Likelihood Training of Schrödinger Bridge Using Forward-Backward SDEs Theory | Guan-Horng Liu

Likelihood Training of Schrödinger Bridge Using Forward-Backward SDEs Theory | Guan-Horng Liu

Geometric Clifford Algebra Networks and Clifford Neural Layers for PDE Modeling

Geometric Clifford Algebra Networks and Clifford Neural Layers for PDE Modeling

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

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

On the Expressive Power of Geometric Graph Neural Networks | Chaitanya K. Joshi & Simon V. Mathis

On the Expressive Power of Geometric Graph Neural Networks | Chaitanya K. Joshi & Simon V. Mathis

Molecule Representation Learning: A Perspective from Topology, Geometry, and Textual Description

Molecule Representation Learning: A Perspective from Topology, Geometry, and Textual Description

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

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

Score Matching via Differentiable Physics | Benjamin Holzschuh

Score Matching via Differentiable Physics | Benjamin Holzschuh

Flow Annealed Importance Sampling Bootstrap | Laurence Midgley and Vincent Stimper

Flow Annealed Importance Sampling Bootstrap | Laurence Midgley and Vincent Stimper

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

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

ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on MD Simulations

ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on MD Simulations

Examining GNNs for Crystal Structures: Limitations and Opportunities for Capturing Periodicity

Examining GNNs for Crystal Structures: Limitations and Opportunities for Capturing Periodicity

On the Ability of Graph Neural Networks to Model Interactions Between Vertices | Noam Razin

On the Ability of Graph Neural Networks to Model Interactions Between Vertices | Noam Razin

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions | Nikos Karalias

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions | Nikos Karalias

Converging Advances to Accelerate Molecular Simulation | Max Welling

Converging Advances to Accelerate Molecular Simulation | Max Welling

GFlowNet Foundations and Applications in Biological Sequence Design | Sebastian Voigtländer

GFlowNet Foundations and Applications in Biological Sequence Design | Sebastian Voigtländer

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design | Ilia Igashov

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design | Ilia Igashov

Towards Good Validation Metrics for Generative Models | Christopher Beckham

Towards Good Validation Metrics for Generative Models | Christopher Beckham

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Predicting Single-Cell Perturbation Responses For Unseen Drugs | Leon Hetzel and Simon Böhm

Predicting Single-Cell Perturbation Responses For Unseen Drugs | Leon Hetzel and Simon Böhm

Roughness of Molecular Property Landscapes and Its Impact on Modellability | Matteo Aldeghi

Roughness of Molecular Property Landscapes and Its Impact on Modellability | Matteo Aldeghi

Towards Better Evaluation for Dynamic Link Prediction | Andy Huang and Farimah Poursafaei

Towards Better Evaluation for Dynamic Link Prediction | Andy Huang and Farimah Poursafaei

Crippen’s logP as a Quantitative Molecular Benchmark for Explainable AI Heatmaps | Jan Jensen

Crippen’s logP as a Quantitative Molecular Benchmark for Explainable AI Heatmaps | Jan Jensen

DiGress: Discrete Denoising Diffusion for Graph Generation | Clément Vignac

DiGress: Discrete Denoising Diffusion for Graph Generation | Clément Vignac

Pre-trained Ensembles for Bayesian Optimization of Protein Sequences | Ziyue Yang

Pre-trained Ensembles for Bayesian Optimization of Protein Sequences | Ziyue Yang

Unsupervised Learning of Group Invariant and Equivariant Representations

Unsupervised Learning of Group Invariant and Equivariant Representations

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

AlphaFold2, OpenFold, Protein Language Models and Beyond | Nazim Bouatta

AlphaFold2, OpenFold, Protein Language Models and Beyond | Nazim Bouatta

Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent

Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent

Diffusion probabilistic modelling of protein backbones in 3D | Jason Yim & Brian Trippe

Diffusion probabilistic modelling of protein backbones in 3D | Jason Yim & Brian Trippe

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

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

Geometric Deep Learning for Drug Discovery | Jian Tang

Geometric Deep Learning for Drug Discovery | Jian Tang

Kernel Methods for Predicting Yields of Chemical Reactions | Jonathan Hirst

Kernel Methods for Predicting Yields of Chemical Reactions | Jonathan Hirst

Geometric diffusions as a tool for harmonic analysis and structure definition of data | Prof Coifman

Geometric diffusions as a tool for harmonic analysis and structure definition of data | Prof Coifman

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization | Tianfan Fu

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization | Tianfan Fu

Bridging Computation and Experimentation with Evidential Deep Learning | Ava Amini

Bridging Computation and Experimentation with Evidential Deep Learning | Ava Amini

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics | Robin Walters

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics | Robin Walters

Machine Learning for Scientific Discovery | Yoshua Bengio

Machine Learning for Scientific Discovery | Yoshua Bengio

Pri3D: Can 3D Priors Help 2D Representation Learning? | Ji Hou

Pri3D: Can 3D Priors Help 2D Representation Learning? | Ji Hou

Graph Neural Networks as Gradient Flows | Francesco Di Giovanni & James Rowbottom

Graph Neural Networks as Gradient Flows | Francesco Di Giovanni & James Rowbottom

Frame Averaging for Invariant and Equivariant Network Design | Omri Puny

Frame Averaging for Invariant and Equivariant Network Design | Omri Puny

e3nn: Euclidean Neural Networks | Mario Geiger

e3nn: Euclidean Neural Networks | Mario Geiger

Pure Transformers are Powerful Graph Learners | Jinwoo Kim

Pure Transformers are Powerful Graph Learners | Jinwoo Kim

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | Nicholas Gao

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | Nicholas Gao

Recipe for a General, Powerful, Scalable Graph Transformer | Ladislav Rampášek

Recipe for a General, Powerful, Scalable Graph Transformer | Ladislav Rampášek

On Recoverability of Graph Neural Network Representation | Maxim Fishman

On Recoverability of Graph Neural Network Representation | Maxim Fishman

Beyond Atoms + Bonds: Contextual Explainability via Molecular Graphical Depictions | Marco Bertolini

Beyond Atoms + Bonds: Contextual Explainability via Molecular Graphical Depictions | Marco Bertolini

Bayesian Modelling of Synergistic Drug Combination Effects in Cancer Using Gaussian Processes

Bayesian Modelling of Synergistic Drug Combination Effects in Cancer Using Gaussian Processes

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric ML | Xiang Fu

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric ML | Xiang Fu

Open Source Initiatives to get you Started with AI in Drug Discovery | Various Speakers

Open Source Initiatives to get you Started with AI in Drug Discovery | Various Speakers

Graph Ordering Attention Networks | Michail Chatzianastasis & Johannes F. Lutzeyer

Graph Ordering Attention Networks | Michail Chatzianastasis & Johannes F. Lutzeyer

Graph-Coupled Oscillator Networks | T. Konstantin Rusch

Graph-Coupled Oscillator Networks | T. Konstantin Rusch

Integrating structure-based and ligand-based modeling for drug design - Joseph M. Paggi

Integrating structure-based and ligand-based modeling for drug design - Joseph M. Paggi

SPECTRE: Spectral Conditioning for One-shot Graph Generators | Karolis Martinkus & Andreas Loukas.

SPECTRE: Spectral Conditioning for One-shot Graph Generators | Karolis Martinkus & Andreas Loukas.

Optimal Transport Modeling of Population Dynamics in Single-Cell Biology - Charlotte Bunne

Optimal Transport Modeling of Population Dynamics in Single-Cell Biology - Charlotte Bunne

Torsional Diffusion for Molecular Conformer Generation | Gabriele Corso & Bowen Jing

Torsional Diffusion for Molecular Conformer Generation | Gabriele Corso & Bowen Jing

Topological Graph Neural Networks | Edward De Brouwer

Topological Graph Neural Networks | Edward De Brouwer

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

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

Generative Coarse-Graining of Molecular Conformations | Wujie Wang

Generative Coarse-Graining of Molecular Conformations | Wujie Wang

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Nets - Philipp Seidl

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Nets - Philipp Seidl

Bayesian Optimization for Ternary Complex Prediction - Noah Weber

Bayesian Optimization for Ternary Complex Prediction - Noah Weber

Theory of Graph Neural Networks: Representation and Learning | Derek Lim

Theory of Graph Neural Networks: Representation and Learning | Derek Lim

Message Passing Neural PDE Solvers | Johannes Brandstetter

Message Passing Neural PDE Solvers | Johannes Brandstetter

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations - Keir Adams

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations - Keir Adams

Local Equivariant Representations for Large-Scale Atomistic Dynamics | S. Batzner and A. Musaelian

Local Equivariant Representations for Large-Scale Atomistic Dynamics | S. Batzner and A. Musaelian

Exposing the limitations of molecular machine learning with activity cliffs - Derek van Tilborg

Exposing the limitations of molecular machine learning with activity cliffs - Derek van Tilborg

Top-N: Equivariant set and graph generation without exchangeability | Clément Vignac

Top-N: Equivariant set and graph generation without exchangeability | Clément Vignac

Graph Attention Retrospective | Kimon Fountoulakis

Graph Attention Retrospective | Kimon Fountoulakis