Drug Discovery

AlphaFold2 by DeepMind achieved atomic-level accuracy in protein structure prediction, solving a 50-year grand challenge in biology. Its deep learning architecture (Evoformer + structure module) predicts protein 3D structures from amino acid sequences with accuracy rivaling experimental methods. Transformed drug discovery by enabling structure-based design for previously intractable targets. 44,000+ citations, awarded the 2024 Nobel Prize in Chemistry.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods.

ChemCrow is an LLM-based agent system that augments large language models with 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. It integrates tools for organic synthesis, drug discovery, and materials design, allowing the agent to plan syntheses, analyze chemical properties, and execute complex chemical tasks through natural language interaction.

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. Here we introduce ChemCrow, an LLM-based agent that integrates 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. ChemCrow successfully plans syntheses, analyzes chemical properties, and executes complex chemical tasks through natural language interaction.

Therapeutics Data Commons (TDC) is a coordinated initiative providing AI-ready datasets and curated benchmarks across the full spectrum of therapeutic modalities (small molecules, biologics, gene therapy) and stages (target identification, hit discovery, lead optimization, manufacturing). Features 100+ datasets across 50+ learning tasks, with standardized evaluation protocols, data splits, and public leaderboards. Supports systematic evaluation of AI methods for drug discovery and development.

Artificial intelligence is poised to enable breakthroughs and discoveries in therapeutic science. Therapeutics Data Commons is a coordinated initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery. The Commons is a resource with AI-solvable tasks, AI-ready datasets, and curated benchmarks, providing an ecosystem of tools, libraries, leaderboards, and community resources.

The Virtual Lab is an AI agent framework that uses LLM-powered researchers in a simulated laboratory environment to collaboratively design and test scientific hypotheses. It was demonstrated by successfully designing new SARS-CoV-2 nanobodies, with AI agents specializing in different scientific roles working together to propose, evaluate, and refine experimental designs.

Science frequently benefits from teams of interdisciplinary researchers, but many scientists do not have easy access to experts from multiple fields. Although large language models (LLMs) have shown an impressive ability to aid researchers across diverse domains, their uses have been largely limited to answering specific scientific questions rather than performing open-ended research. Here we expand the capabilities of LLMs for science by introducing the Virtual Lab, a framework where LLM-powered AI agents collaborate in a simulated laboratory to design and test scientific hypotheses. The Virtual Lab successfully designed new SARS-CoV-2 nanobodies, demonstrating the potential of multi-agent AI systems for open-ended scientific discovery.