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Machine learning directed drug formulation development
Abstract Machine learning (ML) has enabled ground-breaking advances in the healthcare
and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of …
and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of …
Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety
of computational approaches, which are generally classified as ligand-based (LB) and …
of computational approaches, which are generally classified as ligand-based (LB) and …
A de novo molecular generation method using latent vector based generative adversarial network
Deep learning methods applied to drug discovery have been used to generate novel
structures. In this study, we propose a new deep learning architecture, LatentGAN, which …
structures. In this study, we propose a new deep learning architecture, LatentGAN, which …
SMILES-based deep generative scaffold decorator for de-novo drug design
Molecular generative models trained with small sets of molecules represented as SMILES
strings can generate large regions of the chemical space. Unfortunately, due to the …
strings can generate large regions of the chemical space. Unfortunately, due to the …
Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders
Chemical autoencoders are attractive models as they combine chemical space navigation
with possibilities for de novo molecule generation in areas of interest. This enables them to …
with possibilities for de novo molecule generation in areas of interest. This enables them to …
Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace
The interplay between life sciences and advancing technology drives a continuous cycle of
chemical data growth; these data are most often stored in open or partially open databases …
chemical data growth; these data are most often stored in open or partially open databases …
Practical guidelines for the use of gradient boosting for molecular property prediction
Decision tree ensembles are among the most robust, high-performing and computationally
efficient machine learning approaches for quantitative structure–activity relationship (QSAR) …
efficient machine learning approaches for quantitative structure–activity relationship (QSAR) …
Advances with support vector machines for novel drug discovery
Introduction: Novel drug discovery remains an enormous challenge, with various computer-
aided drug design (CADD) approaches having been widely employed for this purpose …
aided drug design (CADD) approaches having been widely employed for this purpose …
[HTML][HTML] Alkaloids in contemporary drug discovery to meet global disease needs
An overview is presented of the well-established role of alkaloids in drug discovery, the
application of more sustainable chemicals, and biological approaches, and the …
application of more sustainable chemicals, and biological approaches, and the …
Sensing and sensitivity: Computational chemistry of graphene‐based sensors
Highly efficient, tunable, biocompatible, and environmentally friendly electrochemical
sensors featuring graphene‐based materials pose a formidable challenge for computational …
sensors featuring graphene‐based materials pose a formidable challenge for computational …