![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
7MB |
![[thumbnail of Supplementary Information]](https://edoc.mdc-berlin.de/style/images/fileicons/other.png) |
Other (Supplementary Information)
88MB |
| Item Type: | Article |
|---|---|
| Title: | AI-enhanced adaptive virtual screening of large libraries for ligand discovery |
| Creators: |
Cecchini, Domiziana  ORCID: https://orcid.org/0009-0004-0089-7652, Nigam, AkshatKumar, Tang, Ming ORCID: https://orcid.org/0000-0001-9308-0238, Reis, Joana, Koop, Matt, Gottinger, Andrea ORCID: https://orcid.org/0000-0002-0859-5569, Nicoll, Callum Robert ORCID: https://orcid.org/0000-0002-2122-9387, Wang, Yao, Jayaraj, Abhilash ORCID: https://orcid.org/0000-0002-3974-6518, Çınaroglu, Süleyman Selim ORCID: https://orcid.org/0000-0001-7120-3540, Törner, Ricarda, Malets, Yehor ORCID: https://orcid.org/0000-0002-3029-3065, Gehev, Minko, Padmanabha Das, Krishna M., Churion, Kelly, Kim, Jongwan, Thomas, Nidhin, Li, Yong, Seo, Hyuk-Soo ORCID: https://orcid.org/0000-0003-0646-2102, Dhe-Paganon, Sirano ORCID: https://orcid.org/0000-0003-0824-5929, Secker, Christopher ORCID: https://orcid.org/0000-0002-7222-536X, Haddadnia, Mohammad, Hasson, Alexander ORCID: https://orcid.org/0000-0003-0815-9203, Li, Minkai, Kumar, Abhishek ORCID: https://orcid.org/0000-0003-3042-2098, Levin-Konigsberg, Roni, Choi, Eun-Bee, Shapiro, Geoffrey I. ORCID: https://orcid.org/0000-0002-3331-4095, Cox, Huel ORCID: https://orcid.org/0009-0004-5808-868X, Sebastian, Luke, Braithwaite, Chelsea ORCID: https://orcid.org/0009-0007-7720-1045, Bashyal, Puspalata, Radchenko, Dmytro S. ORCID: https://orcid.org/0000-0001-5444-7754, Kumar, Aditya, Yang, Lei ORCID: https://orcid.org/0000-0002-3060-0790, Aquilanti, Pierre-Yves, Gabb, Henry ORCID: https://orcid.org/0000-0002-9507-4250, Alhossary, Amr ORCID: https://orcid.org/0000-0002-4470-5817, O'Neill, Eric, Wagner, Gerhard ORCID: https://orcid.org/0000-0002-2063-4401, Aspuru-Guzik, Alán, Moroz, Yurii S., Kalodimos, Charalampos G., Fackeldey, Konstantin, Schuetz, John D., Mattevi, Andrea ORCID: https://orcid.org/0000-0002-9523-7128, Arthanari, Haribabu ORCID: https://orcid.org/0000-0002-7281-1289 and Gorgulla, Christoph ORCID: https://orcid.org/0000-0001-6986-5270
|
| Abstract: | Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development. AdaptiveFlow provides a screening-ready version of the Enamine REAL Space, to our knowledge the largest library of ready-to-dock, drug-like molecules, comprising 69 billion compounds, also available in SELFIES format. An 18-dimensional grid of molecular properties prioritizes promising chemical subspaces, with optional active learning, reducing computational costs by orders of magnitude. AdaptiveFlow integrates >1,500 docking protocols, including GPU-accelerated and ML-based methods, and achieves near-linear scaling on up to 5.6 million CPUs in the Amazon Web Services cloud. We identified nanomolar inhibitors of two disease-relevant targets, ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1. Co-crystal structures provided mechanistic insights into FSP1 inhibition. AdaptiveFlow enables drug discovery at unprecedented scale and supports the development of AI-driven methods. |
| Source: | Nature Biotechnology |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Date: | 1 September 2026 |
| Official Publication: | https://doi.org/10.1038/s41587-026-03217-x |
| PubMed: | View item in PubMed |
| Related to: |
Repository Staff Only: item control page
