Abstract A004: Integrating high-throughput screening with ligand-based pharmacophore modeling and virtual screening strategies to optimize exonuclease 1 inhibitor design
Bibliographic record
Abstract
Abstract Pharmacophores are the specific steric and electronic features of a small molecule that are necessary to ensure interaction with a specific biological target and to trigger or block its biological response. Three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling is a computational approach that utilizes data collected from in vitro experiments and molecular docking to map the shared features of known actives, producing a template that can be used to optimize the physical and chemical properties of lead compounds. Our goal is to generate a 3D-QSAR model for exonuclease 1 (EXO1) inhibition to support the pre-clinical development of a potent, EXO1-specific, small molecule inhibitor and define the molecular interactions that drive inhibitor specificity toward EXO1 instead of related nucleases. Construction of this model will facilitate the development of a potent EXO1 inhibitor that is selective, cancer-specific, and synthetic lethal with BRCAness. Additionally, it will establish a structural foundation for enhancing the selectivity of inhibitors targeting highly similar 5’ nuclease family members, such as FEN1. Here, we describe our approach to integrating the high-throughput and virtual screening data we have collected for ∼365,000 small molecule compounds to map a preliminary 3D-QSAR hypothesis for EXO1 inhibition. Citation Format: Jessica D. Hess, Li Zheng, Binghui Shen. Integrating high-throughput screening with ligand-based pharmacophore modeling and virtual screening strategies to optimize exonuclease 1 inhibitor design [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 A004.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".