Computational approaches streamlining drug discovery
AV Sadybekov, V Katritch - Nature, 2023 - nature.com
Computer-aided drug discovery has been around for decades, although the past few years
have seen a tectonic shift towards embracing computational technologies in both academia …
have seen a tectonic shift towards embracing computational technologies in both academia …
[HTML][HTML] Properties of FDA-approved small molecule protein kinase inhibitors: A 2023 update
R Roskoski Jr - Pharmacological research, 2023 - Elsevier
Owing to the dysregulation of protein kinase activity in many diseases including cancer, this
enzyme family has become one of the most important drug targets in the 21 st century. There …
enzyme family has become one of the most important drug targets in the 21 st century. There …
Evaluation guidelines for machine learning tools in the chemical sciences
Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
[HTML][HTML] Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update
R Roskoski Jr - Pharmacological research, 2024 - Elsevier
Owing to the dysregulation of protein kinase activity in many diseases including cancer, this
enzyme family has become one of the most important drug targets in the 21 st century. There …
enzyme family has become one of the most important drug targets in the 21 st century. There …
Calibrated geometric deep learning improves kinase–drug binding predictions
Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …
Artificial intelligence, machine learning, and drug repurposing in cancer
Z Tanoli, M Vähä-Koskela… - Expert opinion on drug …, 2021 - Taylor & Francis
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs
for new medical indications. Several machine learning (ML) and artificial intelligence (AI) …
for new medical indications. Several machine learning (ML) and artificial intelligence (AI) …
Polypharmacology: The science of multi-targeting molecules
Polypharmacology is a concept where a molecule can interact with two or more targets
simultaneously. It offers many advantages as compared to the conventional single-targeting …
simultaneously. It offers many advantages as compared to the conventional single-targeting …
MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
AS Rifaioglu, R Cetin Atalay… - …, 2021 - academic.oup.com
Motivation Identification of interactions between bioactive small molecules and target
proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target …
proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target …
DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt
a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the …
a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the …