Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
[HTML][HTML] Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Hybrid machine learning approach to predict the site selectivity of iridium-catalyzed arene borylation
E Caldeweyher, M Elkin, G Gheibi… - Journal of the …, 2023 - ACS Publications
The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective
functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine …
functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine …
Dataset design for building models of chemical reactivity
Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …
the development of new synthetic processes via, for example, evaluating hypothetical …
[HTML][HTML] Prospective de novo drug design with deep interactome learning
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …
chemical and pharmacological properties. We present a computational approach utilizing …
High-throughput synthesis provides data for predicting molecular properties and reaction success
J Götz, MK Jackl, C Jindakun, AN Marziale, J André… - Science …, 2023 - science.org
The generation of attractive scaffolds for drug discovery efforts requires the expeditious
synthesis of diverse analogues from readily available building blocks. This endeavor …
synthesis of diverse analogues from readily available building blocks. This endeavor …
[HTML][HTML] Predictive Minisci late stage functionalization with transfer learning
Structural diversification of lead molecules is a key component of drug discovery to explore
chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of …
chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of …
[HTML][HTML] Identifying opportunities for late-stage CH alkylation with high-throughput experimentation and in silico reaction screening
Enhancing the properties of advanced drug candidates is aided by the direct incorporation
of specific chemical groups, avoiding the need to construct the entire compound from the …
of specific chemical groups, avoiding the need to construct the entire compound from the …
A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
S Liu, T Wen, ASL Pattamatta, DJ Srolovitz - arXiv preprint arXiv …, 2024 - arxiv.org
With the advent of ChatGPT, large language models (LLMs) have demonstrated
considerable progress across a wide array of domains. Owing to the extensive number of …
considerable progress across a wide array of domains. Owing to the extensive number of …
[HTML][HTML] Drug discovery and development in the era of artificial intelligence: From machine learning to large language models
S Guan, G Wang - Artificial Intelligence Chemistry, 2024 - Elsevier
Abstract Drug Research and Development (R&D) is a complex and difficult process, and
current drug R&D faces the challenges of long time span, high investment, and high failure …
current drug R&D faces the challenges of long time span, high investment, and high failure …