Uploaded January 2026 | Updated September 2026, 2 weeks ago
Lightning Talks
F.A.D.E. (Fully Agentic Drug Engine): A Conversational AI Platform for Drug Discovery
Xue Zong
Georgia Institute of Technology
PostaNet: Ai-based Framework for Protein Stability Prediction with Experimental Validation
Tianjian Liang
University of Pittsburgh
Genomic Language Models Decipher the Regulatory Code of Splicing for Drug Sensitivity
Xueyang He
University of Rochester
ATLAS Breaks Data Scaling Barriers for MachineLearning via High-Throughput Synthesis and Screening
Imran Haque
Kimia Therapeutics
The Machine Learning in Drug Discovery Symposium was held on November 7, 2025 at the Broad Institute.
This hybrid symposium highlights recent progress in the application of machine learning techniques to drug discovery and brings together researchers working in applying machine learning to target validation, hit identification and optimization, clinical trial design, biologics, and other areas of drug discovery for talks, poster sessions and networking.
For questions about the symposium or interest in sponsoring the 2026 symposium, reach out to mlinddsymposium@broadinstitute.org.
For more information, visit: broadinstitute.org/machine-learning-drug-discovery-symposium/machine-learning-drug-discovery
Copyright Broad Institute, 2025. All rights reserved.
Lightning Talks
F.A.D.E. (Fully Agentic Drug Engine): A Conversational AI Platform for Drug Discovery
Xue Zong
Georgia Institute of Technology
PostaNet: Ai-based Framework for Protein Stability Prediction with Experimental Validation
Tianjian Liang
University of Pittsburgh
Genomic Language Models Decipher the Regulatory Code of Splicing for Drug Sensitivity
Xueyang He
University of Rochester
ATLAS Breaks Data Scaling Barriers for MachineLearning via High-Throughput Synthesis and Screening
Imran Haque
Kimia Therapeutics
The Machine Learning in Drug Discovery Symposium was held on November 7, 2025 at the Broad Institute.
This hybrid symposium highlights recent progress in the application of machine learning techniques to drug discovery and brings together researchers working in applying machine learning to target validation, hit identification and optimization, clinical trial design, biologics, and other areas of drug discovery for talks, poster sessions and networking.
For questions about the symposium or interest in sponsoring the 2026 symposium, reach out to mlinddsymposium@broadinstitute.org.
For more information, visit: broadinstitute.org/machine-learning-drug-discovery-symposium/machine-learning-drug-discovery
Copyright Broad Institute, 2025. All rights reserved.
