Avner Schlessinger
Professor & Director - Pharmacology & Therapeutics Discovery Training Area AI Small Molecule Drug Discovery Center, Icahn School of Medicine at Mount Sinai
Dr. Avner Schlessinger is the Dr. Amy and James Elster Professor of Pharmacological Sciences at the Icahn School of Medicine at Mount Sinai (ISMMS). He is the Director of the AI Small Molecule Drug Discovery Center at ISMMS, and Co-Director of the Disease Mechanisms and Therapeutics (DMT) Training Area in the Mount Sinai Graduate Program. Dr. Schlessinger graduated from Tel Aviv University with a BSc in Biology and Chemistry and earned his PhD in the Department of Biochemistry and Molecular Biophysics at Columbia University. During his PhD, he developed methods for predicting protein structure and function using advanced machine-learning approaches, including artificial neural networks. Following his graduate studies, Dr. Schlessinger received an NIH NRSA postdoctoral fellowship at the Department of Bioengineering and Therapeutic Sciences at UCSF, where he established methods for structure-based drug design and developed tool compounds for membrane proteins. In 2013, he joined the faculty at Mount Sinai, and in 2023, he was promoted to full Professor with tenure. The overall goal of Dr. Schlessinger’s lab is to improve and automate the drug discovery process by integrating approaches in computational chemistry and artificial intelligence, and applying these methods to characterize disease pathways. His lab publishes in the areas of chemical biology, bioinformatics, and drug discovery, as well as in precision medicine and pharmacogenomics. Dr. Schlessinger serves on the Editorial Boards of PLOS Computational Biology and Trends in Pharmacological Sciences, as well as on the Advisory Board of various biotechnology companies and international consortia.
Seminars
- Combine patient-derived, multi-omic data with AI modeling to enable the discovery of underexplored transporter targets linked to neurological disease
- Identify new chemical probes that modulate transporter activity using structure-based virtual screening, molecular modeling, and rational design
- Share how integrative computational and experimental workflows provide a roadmap for expanding the druggable target landscape beyond enzymes and receptors
The application of AI to target protein folding and identification is rapidly evolving, with tools like AlphaFold, and emerging hybrid models offering unprecedented insights into protein structure and function. Yet, the diversity of available technologies, each with distinct strengths and limitations, presents a challenge for teams seeking to integrate these tools into their discovery workflows. This workshop offers a foundational and critical evaluation of the current AI landscape for target enablement, helping attendees navigate the complex trade-offs between accuracy, scalability, interpretability, and experimental validation.
Participants will explore:
- The comparative strengths and weaknesses of leading AI tools for predicting protein folding, binding surfaces, and post-translational modifications
- Tool performance for hard-to-drug targets such as GPCRs, transcription factors, and RNA-binding proteins
- Strategies for integrating AI predictions with experimental methods (e.g. cross-linking, cryo-EM, FRET) to validate cryptic binding pockets and dynamic conformations
- Case studies on the use of AI in target identification and the role of pharmacophore modeling
- Practical considerations for selecting the right AI tool based on target class, data availability, and downstream application in small molecule design
