[HTML][HTML] Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …
industry and research, where it has been utilized to efficiently identify new chemical entities …
Craft of co-encapsulation in nanomedicine: a struggle to achieve synergy through reciprocity
S Bhattacharjee - ACS Pharmacology & Translational Science, 2022 - ACS Publications
Achieving synergism, often by combination therapy via codelivery of chemotherapeutic
agents, remains the mainstay of treating multidrug-resistance cases in cancer and microbial …
agents, remains the mainstay of treating multidrug-resistance cases in cancer and microbial …
[HTML][HTML] Combining machine learning and molecular simulations to predict the stability of amorphous drugs
T Barnard, GC Sosso - The Journal of Chemical Physics, 2023 - pubs.aip.org
Amorphous drugs represent an intriguing option to bypass the low solubility of many
crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase …
crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase …
[HTML][HTML] Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor
The smooth overlap of atomic positions (SOAP) descriptor represents an increasingly
common approach to encode local atomic environments in a form readily digestible to …
common approach to encode local atomic environments in a form readily digestible to …
Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions
Efficient prediction of the partition coefficient (log P) between polar and non-polar phases
could shorten the cycle of drug and materials design. In this work, a descriptor, named< q …
could shorten the cycle of drug and materials design. In this work, a descriptor, named< q …
[HTML][HTML] Images of chemical structures as molecular representations for deep learning
MR Wilkinson, U Martinez-Hernandez… - Journal of Materials …, 2022 - Springer
Abstract Implementing Artificial Intelligence for chemical applications provides a wealth of
opportunity for materials discovery, healthcare and smart manufacturing. For such …
opportunity for materials discovery, healthcare and smart manufacturing. For such …
Towards a machine learned thermodynamics: Exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in …
C Desgranges, J Delhommelle - Molecular Systems Design & …, 2021 - pubs.rsc.org
In this review, we examine how machine learning (ML) can build on molecular simulation
(MS) algorithms to advance tremendously our ability to predict the thermodynamic properties …
(MS) algorithms to advance tremendously our ability to predict the thermodynamic properties …
[HTML][HTML] Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features
B Wang, J Guo, X Liu, Y Yu, J Wu, Y Wang - BMC bioinformatics, 2023 - Springer
Background The human gut microbiome (HGM), consisting of trillions of microorganisms, is
crucial to human health. Adverse drug use is one of the most important causes of HGM …
crucial to human health. Adverse drug use is one of the most important causes of HGM …
A materials science-inspired paradigm to predict the physical stability of amorphous drugs
T Barnard - 2023 - wrap.warwick.ac.uk
Amorphous drugs have gained attention as a promising alternative to crystalline
formulations due to their ability to enhance solubility. However, ensuring the physical …
formulations due to their ability to enhance solubility. However, ensuring the physical …