Deep learning tools to accelerate antibiotic discovery
Introduction As machine learning (ML) and artificial intelligence (AI) expand to many
segments of our society, they are increasingly being used for drug discovery. Recent deep …
segments of our society, they are increasingly being used for drug discovery. Recent deep …
Machine learning and artificial neural network accelerated computational discoveries in materials science
Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as
part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically …
part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically …
The power of deep learning to ligand-based novel drug discovery
II Baskin - Expert opinion on drug discovery, 2020 - Taylor & Francis
Introduction Deep discriminative and generative neural-network models are becoming an
integral part of the modern approach to ligand-based novel drug discovery. The variety of …
integral part of the modern approach to ligand-based novel drug discovery. The variety of …
Machine learning‐assisted search for novel coagulants: When machine learning can be efficient even if data availability is low
Abstract Design of new drugs is a challenging process: a candidate molecule should satisfy
multiple conditions to act properly and make the least side‐effect—perfect candidates …
multiple conditions to act properly and make the least side‐effect—perfect candidates …
Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction
Graph neural networks and convolutional architectures have proven to be pivotal in
improving the prediction of molecular properties in drug discovery. However, this is …
improving the prediction of molecular properties in drug discovery. However, this is …
Modesus: A machine learning tool for selection of molecular descriptors in qsar studies applied to molecular informatics
The selection of the most relevant molecular descriptors to describe a target variable in the
context of QSAR (Quantitative Structure‐Activity Relationship) modelling is a challenging …
context of QSAR (Quantitative Structure‐Activity Relationship) modelling is a challenging …
Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules
E Raush, R Abagyan, M Totrov - Journal of Chemical Information …, 2022 - ACS Publications
We present a graph-convolutional neural network (GCNN)-based method for learning and
prediction of statistical torsional profiles (STP) in small organic molecules based on the …
prediction of statistical torsional profiles (STP) in small organic molecules based on the …
VDAC1-interacting molecules promote cell death in cancer organoids through mitochondrial-dependent metabolic interference
The voltage-dependent anion-selective channel isoform 1 (VDAC1) is a pivotal component
in cellular metabolism and apoptosis with a prominent role in many cancer types, offering a …
in cellular metabolism and apoptosis with a prominent role in many cancer types, offering a …
Deep Neural Network Applications for Bioinformatics
As Deep Learning and Bioinformatics are constantly evolving fields, this review focuses on
four types of Deep Neural Networks; Feedforward, Recurrent, Convolutional and Generative …
four types of Deep Neural Networks; Feedforward, Recurrent, Convolutional and Generative …
[HTML][HTML] Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning
S Nhlapho, MHL Nyathi, BL Ngwenya, T Dube… - Sciences of …, 2024 - etflin.com
In the field of pharmaceutical research, identifying promising pharmaceutical compounds is
a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental …
a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental …