AIMC Topic: Drug Discovery

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Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.

Current topics in medicinal chemistry
Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimiz...

Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use.

Current topics in medicinal chemistry
Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundati...

Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis.

Chemical & pharmaceutical bulletin
Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by...

Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap.

The Journal of toxicological sciences
The interaction between sunlight and drugs can lead to phototoxicity in patients who have received such drugs. Phototoxicity assessment is a regulatory requirement globally and one of the main toxicity screening steps in the early stages of drug disc...

A Comparative Analytical Review on Machine Learning Methods in Drugtarget Interactions Prediction.

Current computer-aided drug design
BACKGROUND: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predict...

[Computational toxicology in drug safety research].

Nihon yakurigaku zasshi. Folia pharmacologica Japonica
The progress of computational toxicology (CompTox) in drug safety research is highly anticipated. CompTox provides toxicity screening methods for drug discovery in the early stages. CompTox also contributes to fostering the application of the princip...

Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer.

Bioinformatics (Oxford, England)
MOTIVATION: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. ...

Improving target-disease association prediction through a graph neural network with credibility information.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their cred...

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor.

Current computer-aided drug design
BACKGROUND: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterat...