SHANGHAI, Sept. 14, 2026 /PRNewswire/ — MoleculeMind, an AI-native bioengineering infrastructure company, announced that research on its proprietary AI-driven reactive atomistic modeling platform, QuantaMind, has been published in Science Advances. The study demonstrates that QuantaMind achieves Density Functional Theory (DFT)-level accuracy while enabling tens-of-nanosecond reactive molecular dynamics (MD) simulations of 10,000-atom complex biomolecular systems. As the pioneering platform to simulate an entire enzyme reaction, it enables AI to “see” the complete process of proton transfer, bond breaking and formation, and complete enzyme catalytic cycles at the atomic scale.

In industrial applications, QuantaMind has also been scaled to simulate reactive systems comprising hundreds of thousands of atoms. Building on MoleculeMind’s capabilities in protein generation and structure prediction, QuantaMind represents a shift in AI-powered molecular R&D from predicting outcomes to understanding processes. By integrating AI-driven generation with physics-based simulation, it provides explainable and verifiable mechanistic insights for drug discovery and enzyme engineering.

From Static Predictions to Process Understanding in AI Molecular R&D

As AI accelerates molecular generation, researchers increasingly face a new challenge: understanding how large numbers of generated candidates will behave in real environments and why they may succeed or fail. Molecular dynamics provides insights into atomic motion, binding stability, conformational changes, transient pockets, and dynamic interactions. However, conventional non-reactive MD cannot capture proton transfer or bond breaking and formation, making reactive MD essential for understanding how molecular reactions actually occur.

Traditional approaches have struggled to balance accuracy, speed, and scale. Classical force fields enable large-scale simulations but have limited accuracy in reactive environments, while quantum mechanical methods such as DFT are computationally prohibitive for large systems. QM/MM approaches introduce additional constraints around reaction regions and boundary conditions. Machine learning force fields (MLFFs) represent the current mainstream direction, but still face the challenge of maintaining stability over extended simulations.

QuantaMind Framework: A Transition-State-Centered Machine Learning Force Field

QuantaMind is MoleculeMind’s self-developed reactive MLFF framework designed to reliably model bond breaking and formation during sustained simulations of large biomolecular systems. Unlike many MLFFs trained primarily on equilibrium structures, QuantaMind incorporates non-equilibrium conformations and transition-state-centered reaction data, allowing it to learn critical states along reaction pathways. It also introduces DFT method classification embeddings, enabling quantum chemistry data generated using different functionals and basis sets to be incorporated into a unified training framework. Latest benchmark results show that QuantaMind can perform a single time-step simulation of a 100,000-atom reactive system in just 0.25 seconds, achieving state-of-the-art (SOTA) performance among leading MLFFs.

From Research to Industrial Applications

In industrial R&D, QuantaMind has already been applied to enzyme engineering and drug discovery. For enzyme engineering, MoleculeMind has used the platform to uncover reaction mechanisms of complex catalytic enzymes, helping guide mutation selection and shift rational design from trial and error toward computationally guided validation. In drug discovery, QuantaMind helps researchers analyze dynamic molecular interactions and understand why different designs produce different outcomes. In one project involving pH-sensitive antibodies with extended half-life, QuantaMind was used alongside other AI design models, and experimental testing showed that one candidate exhibited a dissociation rate at pH 6.0 approximately 62 times faster than at pH 7.4.

“AI-powered molecular R&D has largely focused on what molecules look like and what we can design. QuantaMind goes further by addressing how molecules move, how they react, and why these processes influence outcomes. We believe AI for science should make processes traceable, mechanisms analyzable, and conclusions verifiable. QuantaMind is designed as critical infrastructure connecting molecular design, mechanistic simulation, and experimental validation, helping establish a more complete and credible paradigm for AI-driven molecular R&D,”said Xu Jinbo, Founder of MoleculeMind.

Read the open-access paper in Science Advances: https://doi.org/10.1126/sciadv.aeg3595 

About MoleculeMind:

Founded in 2022, MoleculeMind is an AI-native biological infrastructure company operating at the intersection of AI, biology and engineering. Powered by its proprietary MoleculeOS® platform, the company enables the de novo design and optimization of proteins for therapeutic and industrial applications. MoleculeMind aims to unlock the full functional potential of proteins and accelerate the development of next-generation therapeutics and high-performance bioproducts. https://moleculemind.com

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SOURCE MoleculeMind