Artificial Intelligence in Vaccine and Drug Design

Author(s):  
Sunil Thomas ◽  
Ann Abraham ◽  
Jeremy Baldwin ◽  
Sakshi Piplani ◽  
Nikolai Petrovsky
2018 ◽  
Vol 20 (4) ◽  
Author(s):  
Yankang Jing ◽  
Yuemin Bian ◽  
Ziheng Hu ◽  
Lirong Wang ◽  
Xiang-Qun Xie

2020 ◽  
Author(s):  
Francesca Grisoni ◽  
Berend Huisman ◽  
Alexander Button ◽  
Michael Moret ◽  
Kenneth Atz ◽  
...  

<p>Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 <i>de novo</i> generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass <i>in vitro</i> activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.<b></b></p>


Author(s):  
Adarsh Sahu ◽  
Jyotika Mishra ◽  
Namrata Kushwaha

: The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Science, which has percolated to all the arenas of science and technology, from core engineering to medicines. Thus, AI has found its way for application in the field of medicinal chemistry and heath care. The conventional methods of drug design have been replaced by computer-aided designs of drugs in recent times. AI is being used extensively to improve the design techniques and required time of the drugs. Additionally, the target proteins can be conveniently identified using AI, which enhances the success rate of the designed drug. The AI technology is used in each step of the drug designing procedure, which decreases the health hazards related to preclinical trials and also reduces the cost substantially. The AI is an effective tool for data mining based on the huge pharmacological data and machine learning process. Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system. The review covers various aspects of AI (Machine learning, Deep learning, Artificial neural networks) in drug design. It also provides a brief overview of the recent progress by the pharmaceutical companies in drug discovery by associating with different AI companies.


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