AI Medical Compendium Journal:
Chemical biology & drug design

Showing 1 to 10 of 30 articles

Discovery of novel TRPV1 modulators through machine learning-based molecular docking and molecular similarity searching.

Chemical biology & drug design
The transient receptor potential vanilloid 1 (TRPV1) channel belongs to the transient receptor potential channel superfamily and participates in many physiological processes. TRPV1 modulators (both agonists and antagonists) can effectively inhibit pa...

Explainability and white box in drug discovery.

Chemical biology & drug design
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision pr...

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...

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning.

Chemical biology & drug design
The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of devel...

Machine learning approaches and their applications in drug discovery and design.

Chemical biology & drug design
This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug ...

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.

Chemical biology & drug design
The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secreta...

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods.

Chemical biology & drug design
Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration...

Evaluation of the performance of various machine learning methods on the discrimination of the active compounds.

Chemical biology & drug design
Machine learning (ML) method performances, including deep learning (DL) on a diverse set with or without feature selection (FS), were evaluated. The superior performance of DL on small sets has not been approved previously. On the other hand, the ava...

Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

Chemical biology & drug design
The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body. To evaluate molec...

Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.

Chemical biology & drug design
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...