Machine learning for polymeric materials: an introduction
Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …
researchers are using data science and polymer informatics to design new materials and …
Applications of virtual screening in bioprospecting: facts, shifts, and perspectives to explore the chemo-structural diversity of natural products
Natural products are continually explored in the development of new bioactive compounds
with industrial applications, attracting the attention of scientific research efforts due to their …
with industrial applications, attracting the attention of scientific research efforts due to their …
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space
Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that
protects cells. These peptides share common structural and physicochemical properties and …
protects cells. These peptides share common structural and physicochemical properties and …
DeePred-BBB: A blood brain barrier permeability prediction model with improved accuracy
The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains
homeostasis inside the central nervous system (CNS). The BBB permeability of compounds …
homeostasis inside the central nervous system (CNS). The BBB permeability of compounds …
Machine learning-based models with high accuracy and broad applicability domains for screening PMT/vPvM substances
Q Zhao, Y Yu, Y Gao, L Shen, S Cui… - Environmental …, 2022 - ACS Publications
Persistent, mobile, and toxic (PMT) substances and very persistent and very mobile (vPvM)
substances can transport over long distances from various sources, increasing the public …
substances can transport over long distances from various sources, increasing the public …
Photoelectrochemical properties, machine learning, and symbolic regression for molecularly engineered halide perovskite materials in water
The machine learning techniques are capable of predicting virtual material design space
and optimizing material fabrication parameters. In this article, we construct machine learning …
and optimizing material fabrication parameters. In this article, we construct machine learning …
Gaussian field-based 3D-QSAR and molecular simulation studies to design potent pyrimidine–sulfonamide hybrids as selective BRAF V600E inhibitors
The “RAS-RAF-MEK-ERK” pathway is an important signaling pathway in melanoma.
BRAFV600E (70–90%) is the most common mutation in this pathway. BRAF inhibitors have …
BRAFV600E (70–90%) is the most common mutation in this pathway. BRAF inhibitors have …
Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?
We present a collection of publicly available intrinsic aqueous solubility data of 829 drug‐
like compounds. Four different machine learning algorithms (random forests [RF], LightGBM …
like compounds. Four different machine learning algorithms (random forests [RF], LightGBM …
Fusion-based deep learning architecture for detecting drug-target binding affinity using target and drug sequence and structure
Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …
discovery. Many computational approaches have been proposed due to costly and time …