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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 …
Concepts of artificial intelligence for computer-assisted drug discovery
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …
opportunities for the discovery and development of innovative drugs. Various machine …
Synthetic azo-dye, Tartrazine induces neurodevelopmental toxicity via mitochondria-mediated apoptosis in zebrafish embryos
Abstract Tartrazine (TZ), or E 102 or C Yellow, is a commonly used azo dye in the food and
dyeing industries. Its excessive usage beyond permissible levels threatens human health …
dyeing industries. Its excessive usage beyond permissible levels threatens human health …
Computational approaches in preclinical studies on drug discovery and development
F Wu, Y Zhou, L Li, X Shen, G Chen, X Wang… - Frontiers in …, 2020 - frontiersin.org
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of
drug development in the costly late stage, it has been widely recognized that drug ADMET …
drug development in the costly late stage, it has been widely recognized that drug ADMET …
An international database for pesticide risk assessments and management
Despite a changing world in terms of data sharing, availability, and transparency, there are
still major resource issues associated with collating datasets that will satisfy the …
still major resource issues associated with collating datasets that will satisfy the …
[HTML][HTML] Advances in Artificial Intelligence (AI)-assisted approaches in drug screening
Artificial intelligence (AI) is revolutionizing the current process of drug design and
development, addressing the challenges encountered in its various stages. By utilizing AI …
development, addressing the challenges encountered in its various stages. By utilizing AI …
pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures
Drug development has a high attrition rate, with poor pharmacokinetic and safety properties
a significant hurdle. Computational approaches may help minimize these risks. We have …
a significant hurdle. Computational approaches may help minimize these risks. We have …
Predicting drug metabolism: experiment and/or computation?
Drug metabolism can produce metabolites with physicochemical and pharmacological
properties that differ substantially from those of the parent drug, and consequently has …
properties that differ substantially from those of the parent drug, and consequently has …
Comparison of cramer classification between toxtree, the OECD QSAR Toolbox and expert judgment
Abstract The Threshold of Toxicological Concern (TTC) is a pragmatic approach in risk
assessment. In the absence of data, it sets up levels of human exposure that are considered …
assessment. In the absence of data, it sets up levels of human exposure that are considered …