preprints.org > biology and life sciences > biophysics > doi: 10.20944/preprints202208.0213.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 10 August 2022 / Approved: 11 August 2022 / Online: 11 August 2022 (08:40:37 CEST)
Version 2 : Received: 19 October 2023 / Approved: 19 October 2023 / Online: 19 October 2023 (13:38:05 CEST)

Thanks to the continued development of experimental structural biology and the half-a-century old Protein Data Bank, 2021 saw a big step forward in the development of protein structure prediction with deep learning algorithms. Recently, DeepMinds AlphaFold has determined the structures of ∼ 200 million proteins from 1 million species. The speed of this progress raise the question of what becomes possible for computational drug discovery and design when we have a systems-wide account of the structures and motions of most proteins. Therefore, this article puts forward the concept of a general intermolecular binding affinity calculator (GIBAC): Kd = f(molA, molB, envPara), towards the acceleration of traditional computer-aided drug design (CADD) and artificial intelligence-integrated drug discovery (AIDD), for both small molecules and biologics such as therapeutic proteins.

intermolecular binding affinity; drug target binding affinity; computer-aided drug design (CADD); artificial intelligence-integrated drug discovery (AIDD); machine learning

Biology and Life Sciences, Biophysics

* All users must log in before leaving a comment