AI Research

Building world models at molecular scale

AI Research is at our origin. It's how we turn drug discovery into an engineering problem
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Next-Gen Foundation Models

Our focus is on scaling AI at the intersection of deep learning and physics.

Frontier AI models don't transfer cleanly to drug discovery: training data is scarce and text is insufficient to describe 3D matter & interactions. We build original models that understand the physics of molecules and augment them with synthetic data. We're the first to demonstrate LLM-like scaling laws with this approach.
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Core Research Areas

Inventing New Architectures for Science

Our research is organized around solving fundamental problems in machine learning that are critical for drug discovery. We pioneer novel deep learning architectures and methods with active research and development.

Generative Diffusion Models

Genesis developed Pearl, our leading foundation model for 3D biomolecular structure prediction. Pearl uses a diffusion-based architecture with equivariant neural networks, trained on physics-based synthetic data, to achieve unprecedented precision. Genesis and NVIDIA jointly announced Pearl at GTC 2025.

Pearl

Reinforcement Learning for Goal-Directed Generation

We use advanced reinforcement learning techniques, grounded in physics-based reward functions, to steer our generative models. This allows us to solve complex, multi-objective optimization problems that are intractable with other methods.

Multi-Modal Learning & Prediction

We build multi-modal foundation models that integrate diverse data sources—from 3D structures to bioassay results—to predict properties like potency and selectivity by combining learned structural representations with experimental context.

Geometric & 3D Deep Learning

Our research advances state-of-the-art equivariant networks that inherently understand the physics of molecular systems, ensuring our models respect rotational and translational symmetries.

Agentic Orchestration

We build autonomous workflows to amplify productivity across our organization. Agents can deploy Genesis foundation models and surface top ranked molecules considering multi-parameter optimization objectives.

Generative Diffusion Models
Reinforcement Learning for Goal-Directed Generation
Multi-Modal Learning & Prediction
Geometric & 3D Deep Learning
Geometric & 3D Deep Learning
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the frontier of Molecular AI

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Original research
Engagement with the global research community

Catalyst for Fundamental Research

The Virtuous Cycle of Research and Impact

Pearl
Our work is deeply rooted in real world application, creating a powerful feedback loop that fuels our research agenda.
By deploying our most advanced models on active drug discovery programs, we pressure test their current limits and uncover the next set of fundamental research questions. This problem-first approach ensures our fundamental ML research balances immediate impact with solving challenges beyond the state of the art.

Join Us

A uniquely potent mix of collaborative minds across AI and biotech.

In The News

We are proud to enable the broader ML research ecosystem through publications and open-source contributions, academic collaborations, and conference sponsorships.
December 2, 2025

Genesis Molecular AI Appoints Sergey Edunov as Senior Vice President of Foundation Models, Further Strengthening Leadership in Machine Learning

October 28, 2025

Introducing Pearl: The Next Generation Foundation Model for Drug Discovery

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