AIMC Topic: Drug Discovery

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Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD.

Current drug discovery technologies
BACKGROUND: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applicatio...

An Analysis of QSAR Research Based on Machine Learning Concepts.

Current drug discovery technologies
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions...

An omics perspective on drug target discovery platforms.

Briefings in bioinformatics
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to di...

A novel molecular representation with BiGRU neural networks for learning atom.

Briefings in bioinformatics
Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the t...

DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: An essential part of drug discovery is the accurate prediction of the binding affinity of new compound-protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the tr...

Can computers conceive the complexity of cancer to cure it? Using artificial intelligence technology in cancer modelling and drug discovery.

Mathematical biosciences and engineering : MBE
Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identif...

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Chemical biology & drug design
Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analyti...

Spectrum of deep learning algorithms in drug discovery.

Chemical biology & drug design
Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovat...

Artificial intelligence: a disruptive tool for a smarter medicine.

European review for medical and pharmacological sciences
OBJECTIVE: Although highly successful, the medical R&D model is failing at improving people's health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI...

Network-principled deep generative models for designing drug combinations as graph sets.

Bioinformatics (Oxford, England)
MOTIVATION: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous ch...