Biology subjects

de la Vega de Leon, A.

Publications and source records attributed to de la Vega de Leon, A..

3 recordsLinked to original sources

bioRxiv2023

Do chemical language models provide a better compound representation?

In recent years, several chemical language models have been developed, inspired by the success of protein language models and advancements in natural language processing. In this study, we explore whether pre-training a chemical language model on billion-scale compound datasets, such as Enamine and ZINC20, can lead to improved compound representation in the drug space. We compare the learned representations of these models with the de facto standard compound representation, and evaluate their potential application in drug discovery and development by benchmarking them on biophysics, physiology, and physical chemistry datasets. Our findings suggest that the conventional masked language modeling approach on these extensive pre-training datasets is insufficient in enhancing compound representations. This highlights the need for additional physicochemical inductive bias in the modeling beyond scaling the dataset size.

bioinformatics↗

bioRxiv2022

Improving the assessment of deep learning models in the context of drug-target interaction prediction

AO_SCPLOWBSTRACTC_SCPLOWMachine Learning techniques have been widely adopted to predict drug-target interactions, a central area of research in early drug discovery. These techniques have shown promising results on various benchmarks although they tend to suffer from poor generalization. This is typically related to very sparse and nonuniform datasets available, which limits the applicability domain of machine learning techniques. Moreover, widespread approaches to split datasets (into training and test sets) treat a drug-target interaction as an independent entities, when in reality the drug and target involved may take part in other interactions, breaking apart the assumption of independence. We observe that this leads to overly optimistic test results and poor generalization of out-of-distribution samples for various state-of-the-art sequence-based machine learning models for drug-target prediction. We show that previous approaches to reduce bias in binding datasets focus on drug or target information only and, thus, lead to similar pitfalls. Finally, we propose a minimum viable solution to evaluate the generalization capability of a machine learning model based on the systematic separation of test samples with respect to drugs and targets in the training set, thus discerning the three out-of-distribution scenarios seen at test time: (1) drug or (2) target present in the training set, or (3) neither.

bioinformatics↗

bioRxiv2019

Identification of compounds that rescue otic and myelination defects in the zebrafish adgrg6 (gpr126) mutant

Adgrg6 (Gpr126) is an adhesion class G protein-coupled receptor with a conserved role in myelination of the peripheral nervous system. In the zebrafish, mutation of adgrg6 also results in defects in the inner ear: otic tissue fails to down-regulate versican gene expression and morphogenesis is disrupted. We have designed a whole-animal screen that tests for rescue of both up- and down-regulated gene expression in mutant embryos, together with analysis of weak and strong alleles. From a screen of 3120 structurally diverse compounds, we have identified 68 that reduce versican b expression in the adgrg6 mutant ear, 41 of which also restore myelin basic protein gene expression in Schwann cells of mutant embryos. Nineteen compounds unable to rescue a strong adgrg6 allele provide candidates for molecules that interact directly with the Adgrg6 receptor. Our pipeline provides a powerful approach for identifying compounds that modulate GPCR activity, with potential impact for future drug design.

developmental biology↗