[HTML][HTML] Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning
Antimicrobial resistance has become a critical global health problem due to the abuse of
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
[HTML][HTML] Deep generative models for peptide design
F Wan, D Kontogiorgos-Heintz… - Digital …, 2022 - pubs.rsc.org
Computers can already be programmed for superhuman pattern recognition of images and
text. For machines to discover novel molecules, they must first be trained to sort through the …
text. For machines to discover novel molecules, they must first be trained to sort through the …
Identification of antimicrobial peptides from the human gut microbiome using deep learning
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but
the short lengths of AMPs pose a challenge for computational prediction. Here we combined …
the short lengths of AMPs pose a challenge for computational prediction. Here we combined …
Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning
Motivation Antimicrobial resistance (AMR) is one of the biggest global problems threatening
human and animal health. Rapid and accurate AMR diagnostic methods are thus very …
human and animal health. Rapid and accurate AMR diagnostic methods are thus very …
iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis.
Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional …
Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional …
xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning
J Chen, HH Cheong, SWI Siu - Journal of chemical information …, 2021 - ACS Publications
Cancer is one of the leading causes of death worldwide. Conventional cancer treatment
relies on radiotherapy and chemotherapy, but both methods bring severe side effects to …
relies on radiotherapy and chemotherapy, but both methods bring severe side effects to …
Deep learning to design nuclear-targeting abiotic miniproteins
CK Schissel, S Mohapatra, JM Wolfe, CM Fadzen… - Nature …, 2021 - nature.com
There are more amino acid permutations within a 40-residue sequence than atoms on Earth.
This vast chemical search space hinders the use of human learning to design functional …
This vast chemical search space hinders the use of human learning to design functional …
[HTML][HTML] Engineering selectively targeting antimicrobial peptides
The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the
development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds …
development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds …
[HTML][HTML] Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides
Background Current methods in machine learning provide approaches for solving
challenging, multiple constraint design problems. While deep learning and related neural …
challenging, multiple constraint design problems. While deep learning and related neural …
[HTML][HTML] Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
S Heinen, GF von Rudorff… - The Journal of Chemical …, 2021 - pubs.aip.org
The interplay of kinetics and thermodynamics governs reactive processes, and their control
is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium …
is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium …