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Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry
Aisulu Zh Kabdraisova1,2, Almagul K Umbetova3, Gulfairuz Zh Kairalapova3
1Scientific Research Institute for New Chemical Technologies and Materials, Farabi University, 96a Tole Bi Str., 050012 Almaty, Kazakhstan.
Molecules (Basel, Switzerland)
|June 26, 2026
Summary
Machine learning (ML) accelerates pharmaceutical chemistry by predicting molecular properties and optimizing drug design. This review highlights ML
Area of Science:
- Pharmaceutical Chemistry and Medicinal Chemistry
- Computational Chemistry and Cheminformatics
- Drug Discovery and Development
Background:
- Machine learning (ML) is increasingly vital in pharmaceutical chemistry, aiding molecular design, synthetic feasibility, and structure-property-performance (SPP) relationships.
- ML enables pre-synthesis prediction of physicochemical properties, reaction pathways, and drug performance, reducing empirical experimentation and enhancing chemical space exploration.
Purpose of the Study:
- To review the emerging role and applications of ML in pharmaceutical chemistry.
- To examine ML's impact on understanding structure-property relationships (SPRs) and property-performance relationships (PPRs) for key pharmaceutical endpoints.
- To propose an integrated SPP framework and discuss frontier ML developments and future directions.
Main Methods:
- Structured narrative review design with PRISMA-aligned systematic search elements.
- Evaluation of 101 studies focusing on ML applications in pharmaceutical chemistry.
- Thematic synthesis of heterogeneous ML applications, including SPRs, PPRs, and SPP relationships.
Main Results:
- ML facilitates prediction of solubility, permeability, stability, dissolution, and bioavailability.
- An integrated SPP framework is proposed, connecting molecular structure, properties, and performance with retrosynthetic analysis and feedback loops.
- Frontier developments include molecular foundation models, generative models, equivariant models, and advanced biomolecular interaction and ADMET predictions.
Conclusions:
- ML is transitioning from a predictive tool to a decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development.
- Future progress hinges on hybrid physics-ML models, uncertainty-aware validation, autonomous experimentation, explainable AI, and sustainable design.
- Addressing limitations like dataset leakage, benchmark inconsistency, and validation gaps is crucial for realizing ML's full potential in drug discovery.
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