Acellera Therapeutics, a pioneer in computational chemistry and AI-driven drug discovery, announced the launch of AceForce 1.0, its groundbreaking neural network potential (NNP) model designed to deliver quantum-level accuracy for predicting atomic interactions—an essential factor in identifying promising drug candidates earlier and more efficiently.
“AceForce 1.0 marks a major leap forward by bringing quantum-like accuracy into everyday drug discovery workflows,” said Gianni De Fabritiis, Founder and Chief Executive Officer of Acellera Therapeutics. “Even in this initial release, AceForce 1.0 matches or surpasses state-of-the-art molecular potentials developed over decades. As we expand our training sets, speed up calculations, and refine these AI-driven models, we look forward to empowering scientists to identify promising molecules more quickly, reliably, and affordably.”
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Key Highlights of AceForce 1.0
- Quantum-Level Accuracy: Built on a proprietary training set of many millions of quantum mechanical (QM) calculations, AceForce 1.0 closely mirrors high-level QM methods to provide reliable potential energy surfaces.
- Broad Applicability: AceForce 1.0 supports a wide range of chemical elements and charged molecules, enabling its use across vast areas of drug discovery and chemical space.
- Optimized Efficiency: AceForce 1.0 can run its simulations at roughly twice the speed of previous-generation NNPs.
- Validated via QuantumBind-RBFE: Acellera Therapeutics employed its QuantumBind-RBFE platform to benchmark AceForce 1.0 against publicly available “gold standard” datasets for relative binding free energy (RBFE).
Source: Businesswire