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Review
. 2018 Feb 6;9:74.
doi: 10.3389/fphar.2018.00074. eCollection 2018.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

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Free PMC article
Review

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Rodolfo S Simões et al. Front Pharmacol. .
Free PMC article

Abstract

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Keywords: QSAR; drug design; machine learning; medicinal chemistry; multi-task learning; transfer learning.

Figures

FIGURE 1
FIGURE 1
Main steps involved in drug design, highlighting the use of computational approaches.
FIGURE 2
FIGURE 2
General framework used to plan a study using (A) transfer learning techniques and (B) multi-task learning.
FIGURE 3
FIGURE 3
Schemes used for applying transfer learning approaches.

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