AIMC Topic: Drug Development

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Application of Artificial Intelligence in Drug Discovery.

Current pharmaceutical design
Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery based on big data to precised, knowledgeable data. The pharmaceu...

A Review on an Artificial Intelligence Based Ophthalmic Application.

Current pharmaceutical design
Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development to introducing products to the market, artificial inte...

Role of Artificial Intelligence in Cancer Diagnosis and Drug Development.

Combinatorial chemistry & high throughput screening
Cancer is a vast form of the disease that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably and attack nearby organs. The traditional approaches to cancer diagnosis and drug development have certain limitatio...

DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.

Combinatorial chemistry & high throughput screening
INTRODUCTION: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositio...

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

Combinatorial chemistry & high throughput screening
BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interac...

DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.

Bioinformatics (Oxford, England)
MOTIVATION: In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or...

[Protein modeling and design based on deep learning].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
The accumulation of protein sequence and structure data allows researchers to obtain large amount of descriptive information, simultaneously it poses an urgent need for researchers to extract information from existing data efficiently and apply it to...

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Briefings in bioinformatics
Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert...

Utilizing graph machine learning within drug discovery and development.

Briefings in bioinformatics
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst...