In silico methods and tools for drug discovery
In the past, conventional drug discovery strategies have been successfully employed to
develop new drugs, but the process from lead identification to clinical trials takes more than …
develop new drugs, but the process from lead identification to clinical trials takes more than …
[HTML][HTML] A review on machine learning approaches and trends in drug discovery
P Carracedo-Reboredo, J Liñares-Blanco… - Computational and …, 2021 - Elsevier
Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …
treatment of diseases. In the last years, the approach used in this search presents an …
Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism
T Wang, J Sun, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives
Toxicity prediction is a critical step in the drug discovery process that helps identify and
prioritize compounds with the greatest potential for safe and effective use in humans, while …
prioritize compounds with the greatest potential for safe and effective use in humans, while …
Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
The paper presents a comprehensive overview of the use of artificial intelligence (AI)
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
Identification of hydantoin based Decaprenylphosphoryl-β-d-Ribose Oxidase (DprE1) inhibitors as antimycobacterial agents using computational tools
Tuberculosis (TB) is one of the emerging infectious diseases in the world. DprE1 (
Decaprenylphosphoryl-β-d-ribose 2′-epimerase), an enzyme accountable for …
Decaprenylphosphoryl-β-d-ribose 2′-epimerase), an enzyme accountable for …
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
E Pérez Santín, R Rodríguez Solana… - Wiley …, 2021 - Wiley Online Library
The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Machine learning in arrhythmia and electrophysiology
Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
HergSPred: accurate classification of hERG blockers/nonblockers with machine-learning models
The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in
cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome …
cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome …