Our
science
Our science is based on an extensive academic and industry track record. We have published articles in top journals in the field
Investigating the Conformational Landscape of AlphaFold2-Predicted Protein Kinase Structures
In this work, we investigated the conformational space and utility of protein kinase structures generated by AlphaFold2
Preference Optimization for Molecular Language Models
In this work, we show that we can use Direct Preference Optimization to align generative chemistry models with medicinal chemist preferences
AI for Targeted Polypharmacology: The Next Frontier in Drug Discovery
In this review, we outline the potential as well as the limitations of leveraging AI for designing drugs with targeted polypharmacology
Protein Structure–Based Gene Expression Signatures
Our team previously developed a deep learning model to characterize cell types using structural gene expression signatures
Improving the Efficacy-Safety Balance of Polypharmacology in Multi-Target Drug Discovery
In this review, our team previously described approaches to improve the safety and efficacy balance of drugs with polypharmacology
Learning with Multiple Pairwise Kernels for Drug Bioactivity Prediction
Our team previously developed machine learning models for drug bioactivity prediction based on multiple chemical and genomic data sources
Transcriptomic Profiling of Human Cardiac Cells Predicts Protein Kinase Inhibitor-Associated Cardiotoxicity
Our team previously developed methods to analyze and predict the cardiotoxicity of kinase drugs
Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors
Our team previously launched an international DREAM challenge to predict kinase-ligand interactions
Redefining the Protein Kinase Conformational Space with Machine Learning
Our team previously developed machine learning models for predicting pharmacologically relevant kinase conformational states
Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies
Our team previously developed methods to analyze the chemogenomic landscape of kinases and their inhibitors