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Structure-Activity Relationship

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SAR and QSAR research on tyrosinase inhibitors using machine learning methods.

SAR and QSAR in environmental research
Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we condu...

Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

Chemical biology & drug design
The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body. To evaluate molec...

Towards compound identification of synthetic opioids in nontargeted screening using machine learning techniques.

Drug testing and analysis
The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a...

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact t...

Accelerating the identification of subtype selective inhibitors via Three-Dimensional Biologically Relevant Spectrum (BRS-3D): The monoamine oxidase subtypes as a case study.

Bioorganic chemistry
Subtype-selective drugs are of great therapeutic importance as they are expected to be more effective and with less side-effects. However, discovery of subtype selective inhibitors was hampered by the high similarity of the binding sites within subfa...

DeepSIBA: chemical structure-based inference of biological alterations using deep learning.

Molecular omics
Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs ...

H-RACS: a handy tool to rank anti-cancer synergistic drugs.

Aging
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug s...

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Molecular pharmaceutics
Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alter...