基于神经场的统一全原子分子生成

摘要

基於結構的藥物設計生成模型通常侷限於特定模態，限制了其更廣泛的應用。為解決這一難題，我們提出FuncBind——一個基於計算機視覺的框架，能跨原子系統生成靶點條件化的全原子分子。FuncBind通過神經場將分子表示為連續原子密度，並採用基於分數的生成模型，其現代化架構改編自計算機視覺文獻。這種模態無關的表徵使單一統一模型能夠訓練於從小微分子到大分子的多樣化原子系統，並處理可變原子/殘基數量（包括非經典氨基酸）。在靶點結構條件下，FuncBind在生成小分子、大環肽和抗體互補決定區環方面展現出具有競爭力的計算模擬性能。通過對兩種選定共晶結構的互補決定區H3環進行從頭重新設計，FuncBind還在體外實驗中成功生成新型抗體結合劑。作為最終貢獻，我們引入了用於結構條件化大環肽生成的新數據集與基準測試平台。代碼已開源於https://github.com/prescient-design/funcbind。

English

Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation. The code is available at https://github.com/prescient-design/funcbind.

基于神经场的统一全原子分子生成

Unified all-atom molecule generation with neural fields

摘要

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