PTIm-mAb is a multi-objective, AI/ML-driven antibody design platform that jointly optimizes sequence liabilities, surface aggregation, charge balance, humanness, and predicted binding affinity, and recommends a bispecific architecture in a single workflow.
The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.
M. Erasmus, Daniel Bedinger, Elizabeth Hopkins et al.· Nature Biotechnology· 0 citations
This structured narrative review examines literature identified in PubMed/MEDLINE, Embase, Scopus and Web of Science from database inception, supplemented by citation tracking to find developability risks beyond those encountered with conventional monospecific antibodies.
Ahmad Z. Al Meslamani, A. Jarab, Khadeijah Ahmed Al Marshoodi· Expert Opinion on Biological...· 0 citations
Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. We present Bits to Binders, a global competition evaluating de novo bin...
Clayton W. Kosonocky, Alex M. Abel, Aaron L. Feller et al.· Molecular Systems Biology· 6 citations· ⚡2
Computational peptide-target interaction (PTI) prediction has advanced rapidly as a tool for drug discovery, yet the field lacks agreed evaluation standards. Discrepancies in metrics, negative sampling strategies, and dataset construction across publications currently render cross-study comparisons methodologically int...
William J. Waldock, Guang-Shun Wang, A. Darzi et al.· Frontiers in Bioinformatics· 0 citations
The results extend de novo antibody design from binary antigen-antibody complexes to programmable multi-component complexes, providing a foundation for designing single molecules that combine recognition of distinct targets and their associated biological functions.
Antibody developability is increasingly predictable from sequence, yet software and trained models are rarely made available. We present DELPHI, open software for training developability predictors from labelled antibody assay data, together with ready-to-run, retrainable models. DELPHI compares 25 language-model and c...
Hoan N. Nguyen, Deepash Kothiwal, Yang Su et al.· bioRxiv· 0 citations
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