AIMC Topic: Drug Discovery

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ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction.

Computers in biology and medicine
Effectively predicting protein toxicity plays an essential step in the early stage of protein-based drug discovery, which is of great help to speed up novel drug screening and reduce costs. Recently, several relevant datasets have been designed, and ...

Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor.

Nature communications
The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models ...

Multimodal multi-task deep neural network framework for kinase-target prediction.

Molecular diversity
Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods f...

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques.

Chemical biology & drug design
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computation...

Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies.

Expert opinion on drug discovery
INTRODUCTION: The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approve...

OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.

PLoS computational biology
Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are diffi...

MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.

Journal of chemical information and modeling
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To en...

CADD, AI and ML in drug discovery: A comprehensive review.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequentl...

Application of Computational Biology and Artificial Intelligence in Drug Design.

International journal of molecular sciences
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the...

Prediction of drug-target interactions through multi-task learning.

Scientific reports
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...