DoblesA, JovicN, LeidalK, et al.Pearl: A foundation model for placing every atom in the right location. ArXiv, 2025. Available from: https://arxiv.org/abs/2510.24670
3.
XuZ, RenF, WangP, et al.A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: A randomized phase 2a trial. Nat Med, 2025; 31(8):2602–2610; doi: 10.1038/s41591-025-03743-2
4.
TeamCD, BoitreaudJ, DentJ, et al.Zero-shot antibody design in a 24-well plate. bioRxiv, 2025; doi: 10.1101/2025.07.05.6630182025.07.05.663018;
5.
LinF. Scratch That? De novo antibody design enters the AI drug discovery toolbox. GEN Biotechnol, 2025; 4(3):99–103; doi: 10.1089/genbio.2025.0025
6.
BennettNR, WatsonJL, RagotteRJ, et al.Atomically accurate de novo design of antibodies with RFdiffusion. bioRxiv, 2025:2024.03.14.585103; doi: 10.1101/2024.03.14.585103
RoohaniYH, HuaTJ, TungP-Y, et al.Virtual Cell Challenge: Toward a Turing test for the virtual cell. Cell, 2025; 188(13):3370–3374; doi: 10.1016/j.cell.2025.06.008
9.
DapelloJ, NassarM, EksiR, et al.scGeneScope: A treatment-matched single cell imaging and transcriptomics dataset and benchmark for treatment response modeling. NeurIPS; 2025. Available from: https://openreview.net/forum?id=918POZbZ50
10.
WuY, WershofE, SchmonSM. PerturBench: Benchmarking machine learning models for cellular perturbation analysis. ArXiv, 2024. Available from: https://arxiv.org/abs/2408.10609
11.
DibaeiniaP, GermanC, ShringarpureS, et al.PRSformer: Disease prediction from million-scale individual genotypes. bioRxiv, 2025; doi: 10.1101/2025.10.26.684578
12.
LiuQ, ZhangQ, DuJ, et al.Effects of distance metrics and scaling on the perturbation discrimination score. arXiv, 2025. Available from: https://arxiv.org/abs/2511.16954
