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 …
[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 …
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 …
Fluoroquinolones hybrid molecules as promising antibacterial agents in the fight against antibacterial resistance
The emergence of bacterial resistance has motivated researchers to discover new
antibacterial agents. Nowadays, fluoroquinolones keep their status as one of the essential …
antibacterial agents. Nowadays, fluoroquinolones keep their status as one of the essential …
A machine learning tool to predict the antibacterial capacity of nanoparticles
The emergence and rapid spread of multidrug-resistant bacteria strains are a public health
concern. This emergence is caused by the overuse and misuse of antibiotics leading to the …
concern. This emergence is caused by the overuse and misuse of antibiotics leading to the …
Machine learning in antibacterial drug design
M Jukič, U Bren - Frontiers in Pharmacology, 2022 - frontiersin.org
Advances in computer hardware and the availability of high-performance supercomputing
platforms and parallel computing, along with artificial intelligence methods are successfully …
platforms and parallel computing, along with artificial intelligence methods are successfully …
Success stories of AI in drug discovery-where do things stand?
Introduction Artificial intelligence (AI) in drug discovery and development (DDD) has gained
more traction in the past few years. Many scientific reviews have already been made …
more traction in the past few years. Many scientific reviews have already been made …
First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery
Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)
intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative …
intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative …
Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends
K Diéguez-Santana, H González-Díaz - Computers in Biology and Medicine, 2023 - Elsevier
Abstract Machine learning (ML) methods are used in cheminformatics processes to predict
the activity of an unknown drug and thus discover new potential antibacterial drugs. This …
the activity of an unknown drug and thus discover new potential antibacterial drugs. This …
Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern
GC Verissimo, MSM Serafim… - Expert Opinion on …, 2022 - Taylor & Francis
Introduction Modern drug discovery is generally accessed by useful information from
previous large databases or uncovering novel data. The lack of biological and/or chemical …
previous large databases or uncovering novel data. The lack of biological and/or chemical …