#7: High-throughput Organic Solubility Prediction with FastSolv

Date and Time

• Date: Thursday, October 8, 2026

• Time: 9:00 AM – 10:00 AM EDT / 21:00 – 22:00 (GMT+8)

• Location: Online @ Zoom Webinar

Event Details

Join AI4Life for a technical webinar exploring FastSolv, an open-source machine learning framework for high-throughput organic solubility prediction published in Nature Communications. The session will address the critical bottlenecks of experimental solubility screening and physics-based simulations, explore the representation modeling of solute-solvent interactions, and examine how researchers can leverage rapid, accurate solubility inference across drug discovery, formulation, and chemical R&D workflows.

This webinar will be hosted by Quynh Nguyen from the AI4Life Team. The format includes topic framing, a 20–25 minute technical keynote, an interactive live Q&A session, and a practical platform walkthrough. Across the expanding landscape of AI-driven drug discovery tools, the session will also showcase Vecura - an accessible, browser-based environment where researchers can run state-of-the-art open-source models like FastSolv without coding experience or local GPU infrastructure, supported by an Academic Plan offering 6,000,000 free compute tokens for university researchers.

Speaker Information

Featured speaker: Dr. Lucas Attia, Machine Learning Scientist at Lilly TuneLab.

Dr. Lucas Attia is a machine learning scientist at Eli Lilly, where he develops molecular property prediction and generation models for Lilly TuneLab, a collaborative federated learning platform for drug discovery. He earned his Ph.D. in Chemical Engineering from MIT in 2026, where his research focused on the processing of solubility-enhancing drug nanoparticles and resulted in multiple publications at the interface of nanotechnology, drug formulation, and cheminformatics. Lucas is an accomplished communicator and recipient of numerous honors, including the ACS CAS Future Leaders Award (2026), the MIT Open Data Prize (2025), and the AIChE PD2M Student Award (2025).

What you will learn

• The scientific foundations, data curation challenges, and architectural development behind FastSolv

• Key technical insights into solute-solvent interaction modeling and high-throughput organic solubility inference

• Practical strategies for integrating FastSolv into downstream crystallization, solvent selection, and formulation design workflows

• A live demonstration of running FastSolv on Vecura Workspace: browser-based, no-code execution with zero terminal setup, and how academic researchers can unlock 6,000,000 free compute tokens via the Academic Plan.

Register now to join the live session and bring your questions for the discussion!