| Résumé | Generative Artificial Intelligence is transforming the molecular discovery by enabling exploration of the vast, largely unexplored chemical space. However, current methods, including normalizing flows, struggle to balance the optimization of complex objectives and sampling speed, particularly when generating specific compound classes and more intricate scaffolds, such as aromatic rings. This work developed a generative model that efficiently samples novel molecules while optimizing their drug-likeness, ease of synthesis or chemical reactivity. To achieve this, we employed normalizing flows combined with variational autoencoders to generate samples which were evaluated for the Quantitative Estimate of Drug-likeness, the Synthetic Accessibility scores and, in case of organofluorine-phosphates, electronic density on the central phosphorus atom, approximated by Hirschfeld charges calculated with density functional theory. Our framework efficiently generated a diverse range of organofluorine-phosphates, demonstrating that combining normalizing flows directly with SELFIES or group-SELFIES can address key limitations in inverse molecular design, particularly when variational autoencoders cannot be applied due to a lack of available training data. Normalizing flows capture the chemical structures in a holistic way which paves the way towards targeted therapies that enable the optimization of complex molecular objectives. |
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