AIMC Topic: Drug Discovery

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Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Computational strategies for identifying new drug-target interactions (DTIs) can guide the process of drug discovery, reduce the cost and time of drug development, and thus promote drug development. Most recently proposed methods predict DTIs via int...

CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention.

International journal of molecular sciences
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug-target affinity is crucial. The pr...

Predicting compound-protein interaction using hierarchical graph convolutional networks.

PloS one
MOTIVATION: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop...

Industrializing AI-powered drug discovery: lessons learned from the computing platform.

Expert opinion on drug discovery
INTRODUCTION: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named ' was built up on the initial predicate to capitaliz...

AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists.

European journal of medicinal chemistry
Artificial intelligence (AI) has been recognized as a powerful technique that can accelerate drug discovery during the hit compound identification step. However, most simple deep learning models have been used for naive pre-filtering as the predictio...

Exploring Deep Learning of Quantum Chemical Properties for Absorption, Distribution, Metabolism, and Excretion Predictions.

Journal of chemical information and modeling
Quantum mechanical (QM) descriptors of small molecules have wide applicability in understanding organic reactivity and molecular properties, but the substantial compute cost required for QM calculations limits their broad usage. Here, we investigate...

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer.

Molecules (Basel, Switzerland)
NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory dis...

GraphDPA: Predicting drug-pathway associations by graph convolutional networks.

Computational biology and chemistry
Pathway-based drug discovery is a promising strategy for the discovery of drugs with low toxicity and side effects. However, identifying the associations between drug and targeting pathways is challenging for this method. The formation of various bio...

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks.

Journal of chemical information and modeling
Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (...