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AceFF: New ML Potential for Small Molecule Drug Discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced AceFF, a new machine learning interatomic potential (MLIP) designed for small molecule drug discovery. This model refines the TensorNet2 architecture and is trained on a large dataset of drug-like compounds to balance speed with accuracy. Two versions, AceFF-2 and AceFF-2-OMOL, are available, supporting various chemical elements and charged states, and have demonstrated state-of-the-art performance on benchmarks relevant to drug discovery. AI

IMPACT This new ML potential could accelerate drug discovery by providing faster and more accurate simulations for small molecules.

RANK_REASON The cluster describes a new machine learning model released as an arXiv preprint, detailing its architecture and performance on scientific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AceFF: New ML Potential for Small Molecule Drug Discovery

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The cluster describes a new machine learning model released as an arXiv preprint, detailing its architecture and performance on scientific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Stephen E. Farr, Stefan Doerr, Antonio Mirarchi, Alireza Moradzadeh, Francesc Sabanes Zariquiey, Saee Paliwal, Gianni De Fabritiis ·

    AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules

    arXiv:2601.00581v3 Announce Type: replace-cross Abstract: We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to Density Functional Theory (DFT), generaliza…

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