CLaSP: A Contrastive Learning-Guided Latent Scoring Platform for Comprehensive Drug-Likeness Evaluation

Efficient drug-likeness evaluation is critical for accelerating
drug discovery and reducing the costs of early stage compound screening.
However, existing approaches either rely on rigid empirical rules
or supervised classification models, which lack generalizability and
interpretability. Here we introduce contrastive learning–guided
latent scoring platform (CLaSP), a novel framework that integrates
variational autoencoding with triplet contrastive learning for drug-likeness
assessment. CLaSP constructs a structured latent space informed by
both physicochemical and ADMET-related features, enabling a continuous,
interpretable CLaSP_Score that reflects molecular developability.
The feature set was curated from ADMETlab 3.0 and admetSAR 3.0 and
refined via feature selection. Benchmark evaluations demonstrated
that CLaSP outperformed QED and DBPP-Predictor across multiple data
sets and real-world case studies. Furthermore, CLaSP effectively captured
drug optimization trajectories, as shown in a case study of Wee1 inhibitors.
A user-friendly web portal (https://lmmd.ecust.edu.cn/CLaSP) supports single and batch
analyses for early drug design.