Shape-Based Generative Modeling for de Novo Drug Design
Abstract
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound, followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. The generative design of novel scaffolds and functional groups can cover unexplored regions of chemical space that still possess lead-like properties.
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These intricate and expansive architectures attain a SOTA level of validity and diversity in generated molecules but require extended time for completion.
“Although these architectures attain a SOTA level of validity and diversity in generated molecules, the intricate nature of the process leads to an extension in the time required for completion.”
From PCF-VAE: posterior collapse free variational autoencoder for de novo drug design (2025) · cites this paper as [36]
Several research studies propose intricate and expansive architectures to overcome the limitations of conditional VAEs.
“To overcome these limitations, several research studies35,36 propose intricate and expansive architectures.”
From PCF-VAE: posterior collapse free variational autoencoder for de novo drug design (2025) · cites this paper as [36]
Generative models have been used to compile focused and diversity-oriented chemical libraries, design inhibitors de novo, and perform hit-to-lead optimization, typically in a multiobjective mode.
“Generative models have been used to compile both focused and diversity-oriented chemical libraries,149,150 to design inhibitors de novo,151 and to perform hit-to-lead optimization,152 typically in a multiobjective mode where e.g. selectivity, synthetic accessibility, and drug-like properties are improved simultaneously.153,154”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [150]
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- Authors
- Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G.
- Affiliations
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (ES)
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-02-14
- Volume
- 59
- Issue
- 3
- Pages
- 1205-1214
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2914635984
- Cited by
- 229
- References
- 51
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 34 · 2023: 42 · 2022: 30 · 2021: 51 · 2020: 27 · 2019: 10 · 2017: 1
- FWCI
- 20.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- H2020 Excellent Science (675451)
- European Regional Development Fund
- Ministerio de Econom?a y Competitividad (BIO2017-82628-P, MDM-2014-0370)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- de novo drug design, variational autoencoder, pharmacophore features, SMILES generation, convolutional recurrent neural network, chemical space exploration, molecular scaffolds
- MeSH terms
- Machine Learning, Pharmaceutical Preparations, Hydrogen Bonding, Models, Molecular, Molecular Conformation, Drug Design, Molecular Structure, Quantitative Structure-Activity Relationship, Hydrophobic and Hydrophilic Interactions
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