From Computational Chemistry to Generative Models: A Survey of AI-Driven Small-Molecule Drug Discovery
Year: 2026
Type: preprint
Abstract: Small-molecule drug discovery requires navigating enormous chemical space to identify candidates that are simultaneously potent, selective, synthetically accessible, and acceptable across pharmacokinetic and safety profiles; modern generative AI extends a long computational medicinal chemistry lineage that began with QSAR, pharmacophore modeling, and early structure-based de novo design. This survey focuses primarily on developments from 2017 to 2025, with the literature updated through 31 January 2026. Using a medicinal-chemistry lens, it first establishes the shared machinery of the field: molecular representations such as SMILES, SELFIES, graphs, fingerprints, three-dimensional coordinates, voxels, and latent vectors; commonly used datasets and benchmarks; and graph neural network encoders, including message-passing, directional, and equivariant architectures. We compare five principal generative mechanisms—autoregressive models, variational autoencoders, generative adversarial networks, normalizing flows, and diffusion models—together with reinforcement learning as a major goal-directed optimization framework. Across these approaches, we show that current methods offer complementary rather than interchangeable trade-offs, and we highlight recurring challenges, including validity versus synthesizability, limited prospective experimental validation, mode collapse and diversity loss, stereochemical and physical realism in three-dimensional design, computational cost, data scarcity, and the emerging convergence toward scaffold-conditioned generation, hybrid architectures, developability-aware optimization, and molecular foundation models.
Source: ChemRxiv
Authors: Houman Kazemzadeh, Kiarash Mokhtari, Nazanin Mirzaei, Seyed Reza Tavakoli, Ali Reza Keivanimehr, Shayan Majidifar, Ali Behrad, Farbod Davoodi, Sina Khosravi, Behdad Alikhani, Ali Sabzi, Parham Abed Azad, Arshia Abolghasemi, Afshin Cheraghzade, Siavash Ahmadi, Babak Khalaj, Mohammad Hossein Rohban, Gholamali Aminian, Ali Baheri, Koorosh Aslansefat
Institutions: Tehran University of Medical Sciences, Technische Universität Berlin, Sharif University of Technology, Amirkabir University of Technology, Institute for Advanced Studies in Basic Sciences, Mashhad University of Medical Sciences, University of Tehran, Semnan University of Medical Sciences, Electronics Research Institute, The Alan Turing Institute, University of Hull
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204961973