Abstract A013: Facilitating AI-Based Hit Discovery through the Development of the AIRCHECK Platform
Notice bibliographique
Résumé
Abstract Background: Efficient hit discovery is a critical step in drug development, traditionally achieved through high-throughput screening (HTS). Newer methods such as DNA-Encoded Libraries (DEL) and Affinity Selection Mass Spectrometry (ASMS) offer large-scale, cost-effective alternatives. However, they remain limited by synthesis constraints and costly validation steps. To address these challenges, researchers increasingly turn to artificial intelligence (AI) to predict compound bioactivity and virtually screen vast chemical libraries. Although promising, AI performance depends on the quality and diversity of training data and often struggles with generalization. Moreover, most DEL and ASMS datasets are proprietary, hindering transparency and reproducibility. Methods: To tackle the challenges of AI-based hit discovery, we present the Artificial Intelligence Ready CHEmiCal Knowledge-base (AIRCHECK), an open platform designed to make DEL and ASMS datasets accessible and AI-ready. AIRCHECK enables contributors, especially companies working with these technologies, to share data in a standardized format. The datasets are curated, chemical features extracted, and experimental results analyzed to assign proper labels, allowing even those with limited chemistry expertise to build models without additional data processing. Currently, AIRCHECK hosts around 25 AI-ready DEL datasets from three companies, covering billions of compounds, and EAS-MS (Enantioselective Protein ASMS) datasets involving nearly 80 screened proteins. Results: The AIRCHECK platform also offers baseline artificial intelligence models to support the virtual screening of chemical libraries for potential hits, with open-source codes available. The current model is based on a proof-of-concept for fingerprint-based AI-driven hit discovery, implemented as an ensemble of three Light Gradient Boosting Machines (LGBMs), each trained on a different chemical fingerprint: FCFP4, AtomPair, and Topological Torsion. Predictions are averaged across models to enhance robustness. Post-prediction filtering applies multiple chemistry-based rules to prioritize drug-like candidates. To promote chemical diversity, we use fingerprints and the LeaderPicker algorithm to cluster hits and select representative compounds for experimental testing. The model was trained on a subset (∼375,000 compounds) of a 3-billion-member DEL screened against WDR91. It was then used to virtually screen 37 billion compounds from the Enamine REAL Space library. From 48 top-ranked predictions tested experimentally, seven compounds were confirmed to have binding affinities. Significance: These results highlight the effectiveness of AI-driven hit discovery and validate our efforts to expand access to high-quality data, develop robust AI models, and make them openly available. Looking ahead, we plan to grow AIRCHECK into a collaborative framework that enables contributors to build on these resources using open-source tools and cloud platforms to foster transparency, accessibility, and innovation across the biotech and AI communities. Citation Format: Nabin Bagale, Shaghayegh Reza, James Wellnitz, Rafael M. Couñago, Alexander Tropsha, Benjamin Haibe-Kains, Matthieu Schapira, Cheryl Arrowsmith, Aled Edwards. Facilitating AI-Based Hit Discovery through the Development of the AIRCHECK Platform [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A013.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».