Unleashing the full potential of deep learning in drug discovery.

→ Representation Learning
→ Generative Modeling
→ Causality

Universal representations of biological systems:

Transforming complex data — from the physics of atomistic systems to multimodal omics readouts — into meaningful features to enhance our ability to understand biological and chemical phenomena.

Focus areas
July 14, 2023
Graph Positional and Structural Encoder
March 6, 2023
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
June 16, 2022
Long Range Graph Benchmark
May 25, 2022
Recipe for a General, Powerful, Scalable Graph Transformer

Autonomous agents for scientific discovery:

Bringing scalable generative algorithms and foundation models to drug discovery, laying the groundwork for autonomous execution of flexible workflows to accelerate therapeutic discovery.

Focus areas
June 7, 2023
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
June 5, 2023
Real-World Molecular Out-Of-Distribution: Specification and Investigation
May 11, 2023
Towards Understanding and Improving GFlowNet Training
March 6, 2023
MoDTI: Modular Framework For Evaluating Inductive Biases in DTI Modeling

Increased understanding for improved outcomes:

Unraveling complex biological and chemical interactions to reveal the casual relationships behind molecular perturbations, biological targets, and disease outcomes.

Focus areas
February 11, 2023
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
February 9, 2023
Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model
February 8, 2023
DynGFN: Bayesian Dynamic Causal Discovery Using Generative Flow Networks
November 7, 2022
Object-centric Causal Representation Learning
Focus areas

Careers @ Valence Labs

Unleash your potential and build the future of drug discovery at Valence Labs. Explore open opportunities now.

Apply

More Research

July 14, 2023
Graph Positional and Structural Encoder
June 7, 2023
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
June 5, 2023
Real-World Molecular Out-Of-Distribution: Specification and Investigation
May 11, 2023
Towards Understanding and Improving GFlowNet Training
March 6, 2023
MoDTI: Modular Framework For Evaluating Inductive Biases in DTI Modeling
March 6, 2023
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
February 11, 2023
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
February 9, 2023
Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model
February 8, 2023
DynGFN: Bayesian Dynamic Causal Discovery Using Generative Flow Networks
February 1, 2023
GFlowNets for AI-driven Scientific Discovery
November 7, 2022
Object-centric Causal Representation Learning
October 23, 2022
Multi-Objective GFlowNets
September 26, 2022
Learning GFlowNets From Partial Episodes for Improved Convergence and Stability
July 9, 2022
Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations
June 16, 2022
Long Range Graph Benchmark
June 2, 2022
Weakly Supervised Representation Learning with Sparse Perturbations
May 25, 2022
Recipe for a General, Powerful, Scalable Graph Transformer
October 29, 2021
Properties From Mechanisms: an Equivariance Perspective on Identifiable Representation Learning
October 8, 2021
3D Infomax improves GNNs for Molecular Property Prediction
October 4, 2021
On the Robustness of Generalization of Drug–Drug Interaction Models
June 7, 2021
Rethinking Graph Transformers with Spectral Attention
December 16, 2020
Helixer: Cross-species Gene Annotation of Large Eukaryotic Genomes Using Deep Learning
December 15, 2020
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
October 6, 2020
Directional Graph Networks
May 16, 2020
Geodesics in Fibered Latent Spaces: A Geometric Approach to Learning Correspondences Between Conditions
April 12, 2020
Principal Neighbourhood Aggregation for Graph Nets
May 28, 2019
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
May 28, 2019
Adaptive Deep Kernel Learning
Our StoryResearchCareers
Privacy Policy
Reach out to us at
info@valencelabs.ca