Research
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/ 68
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May 20th, 2025
Virtual Cells: Predict, Explain, Discover
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researc…
May 14th, 2025
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring the space of possible perturbations (e.g.,…
April 23rd, 2025
Scaling Deep Learning Solutions for Transition Path Sampling
April 18th, 2025
Weakly Supervised Latent Variable Inference of Proximity Bias in CRISPR Gene Knockouts from Single-Cell Images
April 14th, 2025
Adaptive teachers for amortized samplers
April 11th, 2025
Efficient Biological Data Acquisition through Inference Set Design
April 9th, 2025
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
March 1st, 2025
Towards Improving Exploration through Sibling Augmented GFlowNets
February 12th, 2025
Random Policy Evaluation for Recovering Policies of Generative Flow Networks
February 11th, 2025
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
February 5th, 2025
Learning Symmetries Through Loss Landscape
February 1st, 2025
Gaussian Process Simplicial Complex Prediction via the Ricci-Hodgelet Representations
December 8th, 2024
Implicit Delta Learning of High Fidelity Neural Network Potentials
November 4th, 2024
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
November 1st, 2024
QGFN: Controllable Greediness with Action Values
October 29th, 2024
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
October 26th, 2024
SAFE setup for generative molecular design
October 19th, 2024
Action abstractions for amortized sampling
October 17th, 2024
Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
October 14th, 2024
Graph Classification Gaussian Processes via Hodgelet Spectral Features
October 8th, 2024
Batched Bayesian optimization with correlated candidate uncertainties
October 6th, 2024
Improved Off-policy Reinforcement Learning in Biological Sequence Design
October 4th, 2024
A call for an industry-led initiative to critically assess machine learning for real-world drug discovery
September 11th, 2024
On the Scalability of GNNs for Molecular Graphs
September 11th, 2024
Automated Discovery of Pairwise Interactions from Unstructured Data
September 10th, 2024
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
June 10th, 2024
Graph Positional and Structural Encoder
June 2nd, 2024
Learning to Scale Logits for Temperature-Conditional GFlowNets
May 31st, 2024
Amortizing intractable inference in diffusion models for vision, language, and control
April 2nd, 2024
Propensity Score Alignment for Unpaired Multimodal Data
March 30th, 2024
Targeted Sequential Indirect Experiment Design
March 27th, 2024
Discrete Probabilistic Inference as Control in Multi-path Environments
November 6th, 2023
DGFN: Double Generative Flow Networks
November 2nd, 2023
Generating QM1B with PySCFipu
October 30th, 2023
Role of Structural and Conformational Diversity for Machine Learning Potentials
October 27th, 2023
Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics
October 18th, 2023
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
October 16th, 2023
Gotta be SAFE: A New Framework for Molecular Design
July 14th, 2023
Graph Positional and Structural Encoder
June 7th, 2023
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
June 5th, 2023
Real-World Molecular Out-Of-Distribution: Specification and Investigation
May 26th, 2023
UNSAT Solver Synthesis via Monte Carlo Forest Search
May 11th, 2023
Towards Understanding and Improving GFlowNet Training
March 6th, 2023
MoDTI: Modular Framework For Evaluating Inductive Biases in DTI Modeling
March 6th, 2023
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
February 11th, 2023
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
February 9th, 2023
Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model
February 8th, 2023
DynGFN: Bayesian Dynamic Causal Discovery Using Generative Flow Networks
February 1st, 2023
GFlowNets for AI-driven Scientific Discovery
November 7th, 2022
Object-centric Causal Representation Learning
October 29th, 2022
Object-centric architectures enable efficient causal representation learning
October 23rd, 2022
Multi-Objective GFlowNets
September 26th, 2022
Learning GFlowNets From Partial Episodes for Improved Convergence and Stability
July 9th, 2022
Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations
June 16th, 2022
Long Range Graph Benchmark
June 2nd, 2022
Weakly Supervised Representation Learning with Sparse Perturbations
May 25th, 2022
Recipe for a General, Powerful, Scalable Graph Transformer
October 29th, 2021
Properties From Mechanisms: an Equivariance Perspective on Identifiable Representation Learning
October 8th, 2021
3D Infomax improves GNNs for Molecular Property Prediction
October 4th, 2021
On the Robustness of Generalization of Drug–Drug Interaction Models
June 7th, 2021
Rethinking Graph Transformers with Spectral Attention
December 16th, 2020
Helixer: Cross-species Gene Annotation of Large Eukaryotic Genomes Using Deep Learning
December 15th, 2020
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
October 6th, 2020
Directional Graph Networks
May 16th, 2020
Geodesics in Fibered Latent Spaces: A Geometric Approach to Learning Correspondences Between Conditions
April 12th, 2020
Principal Neighbourhood Aggregation for Graph Nets
May 28th, 2019
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
May 28th, 2019