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Deep learning framework to predict and generate new fluorescent molecules from experimental data
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-07) Azizi, Mina; Aguirre Soto, Héctor Alán; emipsanchez; Ray, Mallar; Bernal Neira, David Esteban; Mendoza Cortés, José Luis; School of Engineering and Sciences; Campus Monterrey; Flores Tlacuahuac, Antonio
Fluorescent molecules play important roles in biological imaging, diagnostics, and materials science. However, identifying efficient and effective fluorophores remains challenging, as traditional trial-and-error experimentation and in silico computations are both costly and time-consuming. To address this, this thesis presents a deep learn- ing approach to streamline the discovery process by predicting optical properties and generating novel fluorescent molecules directly from experimental data. The study is based on FluoDB, a publicly available dataset collected from the literature, containing over 55,000 fluorophore–solvent pairs with experimentally measured optical prop- erties. Graph Convolutional Network (GCN) models were trained to predict four key optical properties and effec- tively captured complex structure–property relationships, achieving R² values ranging from 0.49 to 0.87 across the different targets. A Conditional Variational Autoencoder (CVAE) was also implemented to generate novel fluores- cent molecules based on solvent identity and target absorption range. In total, 2573 valid and structurally diverse molecules were generated, with a variety of predicted optical behaviors. Together, the predictive model and genera- tive models provide a useful and data-driven approach to accelerate exploration and design of functional fluorescent materials.

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