Drug discovery with explainable artificial intelligence
Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …
prediction of molecular structure and function, and automated generation of innovative …
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 …
Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents
Drug discovery relies on the knowledge of not only drugs and targets, but also the
comparative agents and targets. These include poor binders and non-binders for developing …
comparative agents and targets. These include poor binders and non-binders for developing …
Coloring molecules with explainable artificial intelligence for preclinical relevance assessment
J Jiménez-Luna, M Skalic, N Weskamp… - Journal of Chemical …, 2021 - ACS Publications
Graph neural networks are able to solve certain drug discovery tasks such as molecular
property prediction and de novo molecule generation. However, these models are …
property prediction and de novo molecule generation. However, these models are …
Next generation radiotheranostics promoting precision medicine
Highlights•The fundamentals of radiotheranostics, established therapies, and key clinical
studies.•Future developments: new targets, new radionuclides, new platforms, and …
studies.•Future developments: new targets, new radionuclides, new platforms, and …
Machine learning in drug discovery
G Klambauer, S Hochreiter… - Journal of chemical …, 2019 - ACS Publications
QSAR. Despite this long tradition, machine learning methods gained substantial momentum
recently triggered by the success of deep learning in many application areas. 1 A wide …
recently triggered by the success of deep learning in many application areas. 1 A wide …
Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease
SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late
2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to …
2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to …
Assessment of potassium ion channel during electric signalling in biofilm formation of Acinetobacter baumannii for finding antibiofilm molecule
M Tiwari, S Panwar, V Tiwari - Heliyon, 2023 - cell.com
Acinetobacter baumannii is an opportunistic ESKAPE pathogen which causes nosocomial
infections and can produce biofilms that act as resistant determinants. The role of quorum …
infections and can produce biofilms that act as resistant determinants. The role of quorum …
Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network
T Nikolaienko, O Gurbych… - Journal of Computational …, 2022 - Wiley Online Library
Drug discovery pipelines typically involve high‐throughput screening of large amounts of
compounds in a search of potential drugs candidates. As a chemical space of small organic …
compounds in a search of potential drugs candidates. As a chemical space of small organic …
Anti-Inflammatory and Anti-Diabetic Activity of Ferruginan, a Natural Compound from Olea ferruginea
Inflammation is a complex response of the human organism and relates to the onset of
various disorders including diabetes. The current research work aimed at investigating the …
various disorders including diabetes. The current research work aimed at investigating the …