hitprediction

Hit prediction is a field within computational drug discovery focused on forecasting which chemical compounds are likely to act as hits in a biological assay. A hit is typically a compound that shows activity above a predefined threshold in screening experiments, and hit prediction seeks to prioritize such compounds for further experimental validation, reducing cost and time.

Methods are broadly categorized into structure-based approaches, which use the three-dimensional structure of the biological target or protein, and ligand-based approaches, which rely on information about known active compounds. Structure-based methods include molecular docking and pharmacophore modeling. Ligand-based methods include quantitative structure-activity relationship QSAR models, similarity searching, and machine learning classifiers, as well as modern graph-based neural networks. Hybrid approaches combine multiple signals.

Typical workflow: collect HTS or screening data, curate and preprocess, split data into training and test sets, build models or screening filters, evaluate with metrics such as enrichment factor, ROC-AUC, or precision-recall, and prospectively validate top predictions in follow-up assays. Public data resources like ChEMBL, PubChem BioAssay, and BindingDB support model development, while proprietary pharma datasets are common in industry.

Benefits include reduced experimental burden, faster triage of large chemical libraries, and improved hit rates. Challenges involve data quality and noise in screening results, biases from library composition, overfitting in ML models, and generalization to new chemical space. Reproducibility and interpretability remain active concerns.

Applications extend from early lead discovery and target prioritization to drug repurposing and polypharmacology studies. Emerging trends include active learning to guide iterative screening, integration with omics and pathway data, and advanced deep learning architectures for better transferability across targets.