Computational Intelligence and Biochemical Approaches towards Drug Discovery, Molecular Docking and Tissue Engineering

Dear Colleagues, 

In the current era, drug discovery is a complicated and multistep procedure involving a range of various experiments and clinical testing of potential biomolecules that spans years and costs millions of dollars. Despite investing huge amounts of money and time, the rate of possible success is poor, approximately below 15%. In recent times, the advancement of computational intelligence including machine learning (ML), deep learning (DL) and optimization approaches in the field of drug discovery and molecular docking has increased the speed and success rate of drug discovery in a tremendous fashion. These technologies enable researchers to simulate and predict the interactions between respective biomolecules and receptors, which speed up the drug discovery process along with a better accuracy rate. Various computational methods include different algorithms or frameworks related to decision trees, random forests, support vector machine, neural network, deep learning, cox-regression, hashing, multi-objective optimization-based models that tend to develop variational autoencoder, graph neural network, gene modules, hub gene signatures and many more biomedical models/outcomes. Thus, to detect and cure of various complex and dreadful diseases like Cancer, COVID-19, etc., early, new mathematical and other computational techniques that are beneficial to human being in from medical perspective are most welcome. For improving the effectiveness of the upcoming algorithms/flowcharts, it is our pleasure to explore the use of transfer learning techniques that involve pre-trained models and fine-tuning them for specific tasks. Researchers can investigate the potential of transfer learning for drug discovery and molecular docking using appropriate pre-trained models. In addition, our Special Issue also covers various biochemical techniques useful for tissue engineering, cancer therapy, nanoparticle catalysis, material syntheses as well as nanomedicine that tied into computational learning to some extent.

However, the motto of this topic deals with the collection of review works, original research articles, case studies, extensive wet lab data analysis and further downstream analysis that are expected to contribute a lot towards the development of novel and more effective methods for drug discovery as well as other biochemical prospective and tissue engineering. Specifically, we hope to achieve the following outcomes, but not limited to those only:

• Identifying new ML and DL algorithms for the potential drug discovery and molecular docking.
• Developing novel approaches or modifications on clinical testing or therapeutic values.
• Application of various individual DL models (such as VAE, GNN and GAN) or integrated multi-faceted DL models.
• Integrative study of multi-omics gene signature discovery and potential drug discovery using ML and biostatistical applications.
• Protein design (viz., sequence or structure generative model) and drug discovery.
• Potential therapeutic agents on neurodegenerative diseases (viz., Alzheimer's Disease) by molecular docking along with molecular dynamics simulation study of human and plant-based compounds.
• Bioinformatics analysis of PROTAC or molecular glue applied to pharmaceutical industry.
• Nitroarene reduction for testing nanoparticle catalysts.
• Possible effects of electron-beam, gamma-ray, and X-ray irradiation on the areas of physicochemical properties of the heat-induced gel that was made ready with the salt-soluble pork protein.
• Modification of the surface of bacterial cellulose for various biomedical applications which ranges from drug delivery platforms to tissue engineering techniques.
• Modification of the surface of organic and inorganic biomaterials through the directed irradiation synthesis for enhanced cell adhesion as well as cell proliferation in the respective tissue.

Dr. Saurav Mallik
Dr. Teresa Aditya
Prof. Dr. Kai Wang
Guest Editors

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