To unite artificial intelligence and molecular physics to accelerate the design and discovery of new drugs.
Artificial intelligence is transforming drug discovery, enabling scientists to explore molecular possibilities at unprecedented speed. But designing a promising molecule is only the beginning. Understanding how it behaves, interacts, and evolves in a real physical environment is essential to discovering effective drugs.
At Atomistic Labs, we are bridging the gap between AI-driven molecular design and physics-based molecular simulation.
We believe the future of drug discovery lies in bringing these two worlds together—not as separate steps, but as a continuous, intelligent process where AI proposes, physics evaluates, and both improve through learning.
Our goal is to make drug discovery faster, more accurate, and more efficient by combining the creativity of AI with the scientific rigor of molecular physics.
01
Intelligence Meets Physical Reality
AI can explore vast molecular design spaces and generate promising candidates. Physics provides a deeper understanding of how those molecules behave and interact. Together, they can guide drug discovery beyond what either approach can achieve alone.
02
Beyond Static Structures
Drug molecules and their biological targets are constantly moving. Binding, unbinding, conformational changes, and molecular interactions are dynamic processes. We believe understanding these processes—and their underlying thermodynamics and kinetics—is essential to designing better drugs.
03
From Prediction to Continuous Learning
Molecular simulations should do more than evaluate candidates. They should generate physical insights that help AI models improve their predictions and guide the next generation of molecular designs. We envision a closed-loop discovery process where AI and physics continuously inform and improve one another.
04
Evidence Over Confidence
A promising AI prediction is not the same as a validated drug candidate. We believe computational predictions should be supported by physical evidence, quantified uncertainty, and experimental validation wherever possible. When the evidence is insufficient, our technology should make that limitation clear.
An AI-Powered, Physics-Informed Drug Discovery Platform
TraceDDE brings AI-driven molecular design and advanced molecular simulation together in a unified discovery workflow.
We are developing a platform where AI models generate and optimize molecular candidates, physics-based simulations investigate their molecular behavior, and active learning uses those insights to guide subsequent design decisions.
By integrating molecular structure, binding interactions, conformational dynamics, thermodynamics, and kinetics, TraceDDE aims to help researchers prioritize more promising candidates while reducing unnecessary computational and experimental effort.
At the core of TraceDDE is TRACE, our high-performance molecular simulation engine.
TRACE uses fused adaptive enhanced sampling, integrating sampling updates directly into molecular trajectories to accelerate the exploration of complex molecular landscapes.
Designed for computational efficiency, TRACE provides the physical simulation capabilities needed to investigate molecular interactions and support iterative AI-driven drug discovery.
A Future Where AI Designs and Physics Guides Discovery
We envision a new generation of drug discovery in which AI and molecular physics work together seamlessly—from the first molecular idea to the selection of promising candidates for experimental testing.
Our ambition is simple: make the discovery of better drugs faster, smarter, and more accessible through the power of AI and physics.
