Motivation: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. Results: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. Availability and implementation: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model / Ferraro, Luigi; Scala, Giovanni; Cerulo, Luigi; Carosati, Emanuele; Ceccarelli, Michele. - In: BIOINFORMATICS. - ISSN 1367-4811. - 39:7(2023). [10.1093/bioinformatics/btad432]

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model

Scala, Giovanni;Cerulo, Luigi;Ceccarelli, Michele
Ultimo
2023

Abstract

Motivation: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. Results: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. Availability and implementation: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.
2023
MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model / Ferraro, Luigi; Scala, Giovanni; Cerulo, Luigi; Carosati, Emanuele; Ceccarelli, Michele. - In: BIOINFORMATICS. - ISSN 1367-4811. - 39:7(2023). [10.1093/bioinformatics/btad432]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/946089
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