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Drug Design

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Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Journal of chemical information and modeling
Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, . It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous ...

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

International journal of molecular sciences
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation...

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.

International journal of molecular sciences
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures...

Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains.

International journal of molecular sciences
Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. Th...

AlphaFold2 protein structure prediction: Implications for drug discovery.

Current opinion in structural biology
The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificia...

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage.

Journal of chemical information and modeling
Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roc...

SuHAN: Substructural hierarchical attention network for molecular representation.

Journal of molecular graphics & modelling
Recently, molecular representation and property exploration, with the combination of neural network, play a critical role in the field of drug design and discovery for assisting in drug related research. However, previous research in molecular repres...

Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-I.

Biomolecules
Artificial intelligence (AI) has emerged as a key player in modern healthcare, especially in the pharmaceutical industry for the development of new drugs and vaccine candidates [...].

Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery.

Journal of chemical information and modeling
We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used rec...

The search for new efficient inhibitors of SARS-COV-2 through the drug design developed by artificial intelligence.

Journal of biomolecular structure & dynamics
The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzy...