Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Deep learning methods for molecular representation and property prediction
Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …
predict molecular property through diversified models.•One, two, and three-dimensional …
DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
The rational design of PROTACs is difficult due to their obscure structure-activity
relationship. This study introduces a deep neural network model-DeepPROTACs to help …
relationship. This study introduces a deep neural network model-DeepPROTACs to help …
Transition state theory-inspired neural network for estimating the viscosity of deep eutectic solvents
The lack of accurate methods for predicting the viscosity of solvent materials, especially
those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an …
those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an …
[HTML][HTML] A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation
Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer
origin, occurs in 3‐5 per 100 cancer patients in the United States. Heterogeneity and …
origin, occurs in 3‐5 per 100 cancer patients in the United States. Heterogeneity and …
A review of deep learning-based approaches for detection and diagnosis of diverse classes of drugs
Artificial intelligence-based drug discovery has gained attention lately since it drastically cuts
the time and money needed to produce new treatments. In recent years, a vast quantity of …
the time and money needed to produce new treatments. In recent years, a vast quantity of …
A general use QSAR-ARX model to predict the corrosion inhibition efficiency of drugs in terms of quantum mechanical descriptors and experimental comparison for …
A study of 250 commercial drugs to act as corrosion inhibitors on steel has been developed
by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid …
by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid …
Alkaloids in contemporary drug discovery to meet global disease needs
S Daley, GA Cordell - Molecules, 2021 - mdpi.com
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 …
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumeration
Accurate prediction of pharmacological properties of small molecules is becoming
increasingly important in drug discovery. Traditional feature-engineering approaches …
increasingly important in drug discovery. Traditional feature-engineering approaches …
Convolutional neural networks (CNNs): Concepts and applications in pharmacogenomics
JM Vaz, S Balaji - Molecular diversity, 2021 - Springer
Convolutional neural networks (CNNs) have been used to extract information from various
datasets of different dimensions. This approach has led to accurate interpretations in several …
datasets of different dimensions. This approach has led to accurate interpretations in several …