Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Machine learning for synergistic network pharmacology: a comprehensive overview
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …
understand drug actions and interactions with multiple targets. Network pharmacology has …
Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first
US legislation to advance chemical safety evaluations by utilizing novel testing approaches …
US legislation to advance chemical safety evaluations by utilizing novel testing approaches …
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
During drug development, safety is always the most important issue, including a variety of
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …
The impact of chemoinformatics on drug discovery in the pharmaceutical industry
K Martinez-Mayorga, A Madariaga-Mazon… - Expert opinion on …, 2020 - Taylor & Francis
Introduction: Even though there have been substantial advances in our understanding of
biological systems, research in drug discovery is only just now beginning to utilize this type …
biological systems, research in drug discovery is only just now beginning to utilize this type …
QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV …
The main protease (Mpro) of the SARS‐CoV‐2 has been proposed as one of the major drug
targets for COVID‐19. We have identified the experimental data on the inhibitory activity of …
targets for COVID‐19. We have identified the experimental data on the inhibitory activity of …
Opportunities and challenges using artificial intelligence in ADME/Tox
At the recent Artificial Intelligence Applications in Biopharma Summit in Boston, USA, a
panel of scientists from industry who work at the interface of machine learning and pharma …
panel of scientists from industry who work at the interface of machine learning and pharma …
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery
YL Wang, F Wang, XX Shi, CY Jia, FX Wu… - Briefings in …, 2021 - academic.oup.com
Effective drug discovery contributes to the treatment of numerous diseases but is limited by
high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method …
high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method …
DataWarrior: An evaluation of the open-source drug discovery tool
Introduction: DataWarrior is open and interactive software for data analysis and visualization
that integrates well-established and novel chemoinformatics algorithms in a single …
that integrates well-established and novel chemoinformatics algorithms in a single …
Development of antiepileptic drugs throughout history: from serendipity to artificial intelligence
MG Corrales-Hernández, SK Villarroel-Hagemann… - Biomedicines, 2023 - mdpi.com
This article provides a comprehensive narrative review of the history of antiepileptic drugs
(AEDs) and their development over time. Firstly, it explores the significant role of serendipity …
(AEDs) and their development over time. Firstly, it explores the significant role of serendipity …