Production of a new drug is undoubtedly a time-consuming process and requires a lot of research. Many technologies till date have been contributing to this area but the process is highly complex and requires the use of hybrid techniques. Machine Learning is one such technology that is being used on a large scale in cheminformatic, bioinformatic and other types of drug research studies. With this thought, Department of Biotechnology, Faculty of Engineering and Technology (FET), Manav Rachna International Institute of Research and Studies (MRIIRS, Formerly MRIU) has organized a three-day workshop on ‘Drug Designing and Application of Machine Learning’ from 10th January to 12th January 2018.

The workshop has been designed to provide a theoretical background along with the practical exercises on the computational techniques and machine learning applications in the areas of drug designing. The workshop has participants from other reputed Universities and Colleges which include BITS Pilani, Dubai Campus; Pt. J.L.N Govt. College, Faridabad; Dept. of Chemistry, Delhi University, North Campus; and K.L.M.D.N College for Women, Faridabad.

It was inaugurated in the presence of Dr. Krishna Kant, ED & Dean, FET, MRIIRS; Dr. N.C. Wadhwa, VC, MRIIRS, and Dr. Abhilasha Shrourie, Professor & Head, Department of Biotechnology, FET, MRIIRS. The Resource persons for the workshop are Dr. Mymoona Akhtar, Department of Pharmaceutical Technology, Jamia Hamdard University, New Delhi; and Dr. Kalicharan Sharma, CSIR- Research Fellow, Department of Pharmaceutical Technology, Jamia Hamdard University, New Delhi.

While addressing the participants and faculty, Hon’ble VC Dr. N.C. Wadhwa said, “With the setting of the new bio-molecular lab at MRIIRS, we are aiming that our students and faculty will come out with new research projects that will enhance the process of drug designing in the industry.” Under the guidance and sessions by the revered speakers, the workshop is expected to provide all the participants with the knowledge and experience through hands-on training on various applications of machine learning in drug designing.