The AI for Scientific Discovery Summit, presented by SandboxAQ, brings together customers, partners, and scientific leaders across BioSim and ChemSim to explore how AI is accelerating discovery across drug discovery, chemistry, biology, and materials innovation.
Through customer stories, product insights, and peer discussion, the event will highlight how physics-grounded AI and large quantitative models are helping research teams move faster, make better decisions, and advance scientific progress across both molecular and materials R&D.
Specializing in knowledge graph construction and machine learning for drug discovery, leading development of the AI Sim knowledge graph platform and cheminformatics/ML tooling (Shortpath, ModelForge) that power target identification and biomarker discovery efforts like the MJFF Parkinson's disease program
Leads product and ecosystem strategy for SandboxAQ’s core scientific AI assets, transforming proprietary Large Quantitative Models (LQMs) and physics-based datasets into enterprise-grade discovery tools deployed via AWS, GCP, Azure, and MCP. Directs a cross-division portfolio spanning drug and materials discovery, backed by more than a decade of work across life sciences and advanced materials.
Works at the intersection of AI, physics-based simulation, and materials science to accelerate the discovery and commercialization of next-generation semiconductor materials. Her work spans critical innovation areas including catalysts, PFAS-free process chemicals, magnets, and battery systems, and she has spoken publicly on AI-accelerated materials engineering and digital tools for operational efficiency.
Rajeev Chaudhary is a Staff Technical Product Manager at SandboxAQ, where he leads the development of AI-driven simulation tools for drug discovery. He focuses on advancing the adoption of Large Quantitative Models (LQMs) and physics-based simulations to help biopharma partners reduce R&D timelines, improve hit rates, and accelerate drug development. His work spans drug-target interaction prediction, toxicity modeling, and multi-objective molecule optimization.
Brings nearly 40 years of experience in organizational development and technology introduction, including more than 15 years in life sciences. Internal notes reference prior experience at Biogen, the Broad Institute, and Bristol Myers Squibb, along with work advising on commercialization, knowledge graphs, customer community building, and strategic partnerships.
Currently, drug discovery teams using physics-based virtual screening drug discovery teams spend weeks managing GPU infrastructure and writing orchestration code. SandboxAQ connects Large Language Models to Large Quantitative Models via the Model Context Protocol to eliminate this friction. You can execute complex computational workflows using plain English. During this session, we will demonstrate AQPotency, our CPU-based potency prediction ranking engine. We will rank 10,000 protein-ligand pairs in a minute and reverse-screen curated panels for off-target safety signals. You prioritize drug candidates and advance therapies to market without specialized engineering support.
High-fidelity simulations like Density Functional Theory (DFT) drive modern R&D but are bottlenecked by complex coding and massive infrastructure demands. By integrating Large Quantitative Models (LQMs) directly into conversational AI like Anthropic's Claude, we remove this barrier entirely. During this session, we will demonstrate the LLM-to-LQM pipeline using AQCat Adsorption, a spin-aware machine learning engine that delivers near-DFT accuracy at up to 20,000 times the speed of traditional methods. We will show how translating plain-English prompts into automated computational workflows empowers your non-computational bench scientists to independently screen candidates, slash computational overhead, and accelerate physical breakthroughs without writing a single line of code.
SandboxAQ is making its Large Quantitative Models accessible through Google Cloud, allowing users to tap rigorous, physics-grounded scientific models directly from the conversational AI tools they already use, with no specialized code or infrastructure required.
This session will highlight AQCat for materials and catalyst discovery, which targets the critical first step of adsorption energy calculation to help researchers rapidly identify and prioritize the most promising candidates before committing costly modeling and lab resources to full evaluation. By pairing the reasoning of a frontier model with the quantitative precision of SandboxAQ’s Large Quantitative Models, researchers can unlock materials screening at a scale that was previously out of reach.
SandboxAQ’s CHIPS work will showcase how physics-based AI can move scientific discovery beyond prediction and into national-scale industrial impact. In this session, Shalini Sharma will discuss how SandboxAQ is using its ReAQT platform and Large Quantitative Models to accelerate discovery, validation, and commercialization across four semiconductor-critical materials areas: PFAS-free process chemicals, catalysts, rare earth-free magnets, and battery systems. The talk will explore how AI-driven materials workflows can compress years of trial-and-error into targeted discovery campaigns, help reduce dependence on foreign-controlled supply chains, and create a path from breakthrough science to domestic manufacturing partnerships.
SandboxAQ is a science-first technology company spun out of Alphabet in 2022. Our drug discovery team includes a dedicated biopharma core of 70+ specialists. These include over 48 domain PhDs across computational chemistry, medicinal chemistry, computational biology, bioinformatics– who work alongside AI and software engineers specialized in cloud-scale molecular simulation.
