n traditional methods of drug design, searching for a drug in a haystack that exhibits desired biological activities while conforming to safe pharmacological profiles can be a long, costly, and challenging task. Complex methods are employed to identify new chemical compounds that may be developed to be marketed as drugs. Despite all the technological progress, the process is very long, with an estimated average of 9 to 12 years, and the success rate is low, considerably increasing the cost. Recent advances in artificial intelligence (AI) have brought a revolution in today’s drug discovery process, ranging from target identification and lead searching, to safety profile prediction. Computational techniques, such as cheminformatics, can be used to extract meaningful features from chemical structures of large compound databases. In conjunction with machine learning models, quantitative structure-activity relationships (QSARs) can be established to infer new drug activity, inverse drug design, and drug repurposing. Molecular profiling is now able to stratify diseases into their distinct molecular subtypes for matching with appropriate drugs, thus beginning to shape a translational systems medicine for better-tailored predictive and pharmacotherapeutic guidance. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the huge amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs, which can be repurposed for alternative use in medicine.

Submission Guidelines

Guidelines For Abstract:

All the abstracts must be submitted via easychair Link:

At the time of ABSTRACT SUBMISSION, submit the following information:

Title---Make sure that it matches, the core theme of the book

Author details- Name, Department Name, Institute Name and Email Address

Abstract --- Min 200-250 words

6 Keywords

Table of Contents- Tentative

Guidelines For Chapter: 

All Chapters should be min 30-40 Pages, without references and Plagiarism should be less than 20%. And min 50-90 References, English Language should be good. And should comprehensively cover the content. 

List of Topics

1. Introduction to AI and Machine Learning

2. Applications of Deep Learning for Computational Chemistry

3. Introduction to Cheminformatics

4. Understanding of Molecular docking in the age of AI

5. Introduction to Bioinformatics

6. Process and applications of Structure-based drug design (SBDD)

7. Machine learning models for drug target identification

8. AI models for drug property prediction

9. AI models for drug resistance prediction

10. Deep learning models for target protein structure prediction

11. AI-aided drug design

12. AI-based personalized drug treatment

13. Machine learning models for drug efficacy metrics

14. AI models for biopharmaceutical properties prediction

15. Machine learning applications for drug repurposing

Committees

Abhirup Khanna

Mailing Address: ENERGY ACRES, UPES, BIDHOLI, via, Prem Nagar, Uttarakhand 248007, India; Phone No: +91-9759617150; Email: abhirupkhanna@yahoo.com

May ElBarachi

Director, Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, UOWD Building, Dubai Knowledge Park, Dubai, United Arab Emirates, P.O. Box 20183

Phone no: +971 561211784; Email: MaiElbarachi@uowdubai.ac.ae

Sapna Jain

Mailing Address: ENERGY ACRES, UPES, BIDHOLI, via, Prem Nagar, Uttarakhand 248007, India; 

Phone No: +91-9990049256; Email: sapnaj22@gmail.com

Manoj Kumar

Associate Professor, Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, UOWD Building, Dubai Knowledge Park’ Dubai, United Arab Emirates, P.O. Box 20183

Phone No: +971- 529291824; Email: ManojKumar@uowdubai.ac.ae

Dr. Anand Nayyar

Professor, Scientist, Vice-Chairman (Research), Director (IoT and Intelligent Systems Lab), School of Computer Science, Duy Tan University, Da Nang, Viet Nam.

Email: anandnayyar@duytan.edu.vn; Mobile: +84-933622812

Publication

AIMLDD2023 proceedings will be published in Wiley. Indexed in Scopus and Web of Science

Contact

All questions about submissions should be emailed to:

Dr. Anand Nayyar, Duy Tan University, Da Nang 550000, Viet Nam. Mobile: +91-9878327635; Email: anandnayyar@duytan.edu.vn