[HTML][HTML] Computational approaches in preclinical studies on drug discovery and development
F Wu, Y Zhou, L Li, X Shen, G Chen, X Wang… - Frontiers in …, 2020 - frontiersin.org
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of
drug development in the costly late stage, it has been widely recognized that drug ADMET …
drug development in the costly late stage, it has been widely recognized that drug ADMET …
Deep learning in drug discovery
E Gawehn, JA Hiss, G Schneider - Molecular informatics, 2016 - Wiley Online Library
Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …
[HTML][HTML] Transformer-CNN: Swiss knife for QSAR modeling and interpretation
We present SMILES-embeddings derived from the internal encoder state of a Transformer
[1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] …
[1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] …
Learning to navigate the synthetically accessible chemical space using reinforcement learning
Over the last decade, there has been significant progress in the field of machine learning for
de novo drug design, particularly in generative modeling of novel chemical structures …
de novo drug design, particularly in generative modeling of novel chemical structures …
Interpretation of quantitative structure–activity relationship models: past, present, and future
P Polishchuk - Journal of Chemical Information and Modeling, 2017 - ACS Publications
This paper is an overview of the most significant and impactful interpretation approaches of
quantitative structure–activity relationship (QSAR) models, their development, and …
quantitative structure–activity relationship (QSAR) models, their development, and …
Time-split cross-validation as a method for estimating the goodness of prospective prediction.
RP Sheridan - Journal of chemical information and modeling, 2013 - ACS Publications
Cross-validation is a common method to validate a QSAR model. In cross-validation, some
compounds are held out as a test set, while the remaining compounds form a training set. A …
compounds are held out as a test set, while the remaining compounds form a training set. A …
[HTML][HTML] Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Y Zhang - Chemical science, 2019 - pubs.rsc.org
Predicting bioactivity and physical properties of small molecules is a central challenge in
drug discovery. Deep learning is becoming the method of choice but studies to date focus on …
drug discovery. Deep learning is becoming the method of choice but studies to date focus on …
Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …
used infrequently or not at all in chemoinformatics. Machine learning methods are …
In silico ADMET prediction: recent advances, current challenges and future trends
There are numerous small molecular compounds around us to affect our health, such as
drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over …
drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over …
ToxAlerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions
The article presents a Web-based platform for collecting and storing toxicological structural
alerts from literature and for virtual screening of chemical libraries to flag potentially toxic …
alerts from literature and for virtual screening of chemical libraries to flag potentially toxic …