AIMC Topic: Drug Discovery

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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...

Deep generative molecular design reshapes drug discovery.

Cell reports. Medicine
Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user fa...

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

International journal of molecular sciences
Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equati...

Introduction to the Theme "Development of New Drugs: Moving from the Bench to Bedside and Improved Patient Care".

Annual review of pharmacology and toxicology
Investigations in pharmacology and toxicology range from molecular studies to clinical care. Studies in basic and clinical pharmacology and in preclinical and clinical toxicology are all essential in bringing new knowledge and new drugs into clinical...