About
Unlock faster, higher-confidence hit discovery with SandboxAQ’s AI- and physics-driven Virtual Screening Platform.

Traditional high-throughput screening and first-generation in silico methods struggle to efficiently explore today’s vast chemical spaces, often requiring millions of physical assays to find a small number of promising hits. SandboxAQ’s AQBioSim platform and Large Quantitative Models (LQMs) address this gap by combining generative AI, physics-based simulation, and biological context into an integrated, end-to-end virtual screening workflow that can prioritize a small number of high-value compounds for testing while expanding accessible chemical space from millions to billions of candidates.

In this webinar, we will walk through how SandboxAQ’s virtual screening workflow combines protein and library preparation, retrospective validation, and multi-modal screening (shape, similarity, docking, ML-guided methods, and optional generative chemistry) with active-learning-driven enrichment and early ADMET/developability filters. You will see how this approach has delivered step-change improvements in hit rates, reduced false positives, and materially lowered experimental burden across real-world collaborations, including campaigns that achieved more than 20× expansion in ligands explored and over 30× improvement in hit rate versus traditional screening alone.

Key Topics:

  • Understand the core building blocks of SandboxAQ’s virtual screening workflow—from target and library preparation through retrospective validation, protocol selection, large-scale screening, and iterative enrichment—and how these components integrate into existing discovery pipelines.

  • Learn how combining quantum physics–enhanced simulations, LQMs, and multi-modal screening enables exploration of much larger chemical spaces, improves hit-identification efficiency, and reduces false positives compared with traditional docking-only approaches.

  • Review concrete case studies across therapeutic areas and target classes (e.g., CNS, neurodegeneration, and GPCR programs) where virtual screening campaigns delivered enriched hit lists, higher hit rates, and faster progression from hit identification into hit-to-lead and lead optimization.

  • Learn how to assess whether a program is a strong fit for SandboxAQ’s standard virtual screening service or should draw on SandboxAQ’s experience to develop a customized approach.

Presenters
1776803285-3e304cc3a42554bf
Andrea Bortolato, PhD
Vice President, Drug Discovery, SandboxAQ
Andrea Bortolato brings over 20 years of experience in computational chemistry and drug discovery. He has worked in biotechnology, pharmaceuticals, and agrochemistry throughout his career, holding more than 50 scientific patents and publications, including three in Nature. Andrea holds a PhD in computational chemistry, earned in partnership between the University of Padua in Italy and the University of Geneva in Switzerland. He then completed a postdoctoral fellowship at Mount Sinai School of Medicine in New York City.
1780098344-4e2be209eddd5b63
Benjamin J. Shields
Benjamin J. Shields is a Senior Staff Machine Learning Engineer at SandboxAQ, where he leads cheminformatics and machine learning platform development and contributes to drug discovery and diagnostics projects. Benjamin received his PhD in chemistry from Princeton University and has spent his career focusing on accelerating chemical synthesis and drug discovery with AI/ML, computational chemistry, and software development.
Register To Watch Recording
First Name*
Last Name*
Email Address*
Country / Region*
Company / Organization*
Job Level*
utm_bmcr_source
Do you have a question for SandboxAQ that you would like discussed during the webinar? Ask it here:
Registration Terms
This event is hosted by Scientist.com. By registering and participating, you acknowledge that your personal data will be processed by Scientist.com. You also agree to receive email communication from Scientist.com about this webinar and other programs of similar nature. The sponsor of this webinar is SandboxAQ; by registering and participating, you acknowledge that your data will be processed in accordance with SandboxAQ's Privacy Policy. You will receive email communication from SandboxAQ about this webinar and programs of similar interest. You can withdraw your consent at any time from these communications.
Yes, I consent to the registration terms.*
Yes, I consent to the registration terms.*
We use BigMarker as our webinar platform. By clicking Register, you acknowledge that the information you provide will be transferred to BigMarker processing in accordance with their Terms of Service and Privacy Policy.