[HTML][HTML] Artificial intelligence and machine learning in drug discovery and development

V Patel, M Shah - Intelligent Medicine, 2022 - Elsevier
The current rise of artificial intelligence and machine learning has been significant. It has
reduced the human workload improved quality of life significantly. This article describes the …

Antimicrobial resistance crisis: could artificial intelligence be the solution?

GY Liu, D Yu, MM Fan, X Zhang, ZY Jin, C Tang… - Military Medical …, 2024 - Springer
Antimicrobial resistance is a global public health threat, and the World Health Organization
(WHO) has announced a priority list of the most threatening pathogens against which novel …

Machine learning and artificial intelligence: a paradigm shift in big data-driven drug design and discovery

P Pasrija, P Jha, P Upadhyaya… - Current Topics in …, 2022 - benthamdirect.com
Background: The lengthy and expensive process of developing a novel medicine often takes
many years and entails a significant financial burden due to its poor success rate …

Polymeric carriers designed for encapsulation of essential oils with biological activity

AP Chiriac, AG Rusu, LE Nita, VM Chiriac, I Neamtu… - Pharmaceutics, 2021 - mdpi.com
The article reviews the possibilities of encapsulating essential oils EOs, due to their multiple
benefits, controlled release, and in order to protect them from environmental conditions …

In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach

H Yabuuchi, K Hayashi, A Shigemoto, M Fujiwara… - Scientific Reports, 2023 - nature.com
Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide
fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to …

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol …

A Tarazona, EM Mateo, JV Gómez, R Gavara… - International journal of …, 2021 - Elsevier
Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone
(ZEA) and fumonisins (FUM) in different points of the food chain. Application of antifungal …

Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space

H Yabuuchi, K Hayashi, A Shigemoto, M Fujiwara… - Plos one, 2023 - journals.plos.org
Plant extract is a mixture of diverse phytochemicals, and considered as an important
resource for drug discovery. However, large-scale exploration of the bioactive extracts has …

Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

NE El-Attar, MK Hassan, OA Alghamdi, WA Awad - Scientific Reports, 2020 - nature.com
Reliance on deep learning techniques has become an important trend in several science
domains including biological science, due to its proven efficiency in manipulating big data …

Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition–Activity …

R Astolfi, A Oliva, A Raffo, F Sapienza… - Journal of Chemical …, 2025 - ACS Publications
Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical
application is hindered by challenges, such as variability in chemical composition and …

Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils

H Yabuuchi, M Fujiwara, A Shigemoto, K Hayashi… - Scientific Reports, 2024 - nature.com
Plants are valuable resources for drug discovery as they produce diverse bioactive
compounds. However, the chemical diversity makes it difficult to predict the biological activity …