Talk about an Academic Comeback: Dive Deep into Dr. Bajarang’s Exciting Journey in Mastering Computational Chemistry

Dr. Bajarang Kumbhar, an Assistant Professor at SVKM's NMIMS University, is a dedicated educator and researcher specializing in pharmaceutical chemistry. With a strong and extensive research experience, he focuses on advancing the understanding of drug design, synthesis, and analytical techniques. Dr. Kumbhar is passionate about mentoring students and guiding them in innovative research projects. His contributions to academic publications and conferences demonstrate his commitment to fostering scientific excellence and driving innovation in pharmaceutical education and research.

Mr. Ravindra: Welcome Dr. Bajarang to Pharma Now. So, I think we are discussing about your journey. So, for our audience, I would like to understand being a professor of NMIMS. Why you opt for biochemistry and pharma as your area of specialization? 

Dr. Bajarang: Yeah, thank you. Thank you, Pharma Now, for having this opportunity. And I'm happy to give the idea about how I started my career and how I'm currently in biochemistry and computational biology, which is very close to the heart of Pharma Now. So basically, I did my graduation in chemistry. So pharma and chemistry is a very good relationship. So I started my career with chemistry as an undergraduate. but unfortunately to get admission in chemistry only. And that's why I moved my career to biochemistry. 

But I feel that biochemistry is very close to the chemistry. But when I started my study, I realized that it is a life science. It is not a chemistry. But the chemical reactions happening inside the cell require a lot of knowledge about chemistry, physics, a lot of information is required. And I started this particular area is very interesting. Even though I started, I feel that in the first semester of the year, I feel that I don't have confidence that I can easily get the MSc degree in Biochemistry. But with respect to that, I love how DNA interacts, how DNA undergoes changes that kills the protein. And the protein, again, plays a very important role in a lot of biochemical reactions. 

So protein is a key player. DNA is a key player. now then I understand why not I can work on this. So I decided to go for the PhD. But my knowledge of the chemistry is very important. I always think that I will miss the chemistry. And that's why I started my PhD in computational chemistry, where I use the quantum mechanics to understand the conformational state of the biomolecules. Here I use the tRNAs, one of the biomolecules and its interactions with the RNA molecules. where I started learning more about the computational and chemistry part. Because I am a hardcore chemist and then I realized that I again do the same things using computational approach. Then I use quantum mechanics, molecular dynamic simulations. 

So what is quantum mechanics and why I use that field? That is the question. So if you look at the quantum mechanics, quantum mechanics give the atom level or electron level interactions. So, molecular recognition entirely depends upon what type of interactions are there. I will connect this part to my current goal where I use this knowledge. So, atom level or electron level interactions play important role and I use the model system which is here, tRNA. 

So, tRNA have a very interesting part, 34, 35, 30, Hubble base is there. But 37 is something modifications are there that actually control the frame shoot mutation. And that is one of the reason that most of the cell if have preemption mutation, means there is no modifications are there. What is the role of that in decoding the mRNA codon that i studied? 

And that using only competition approach how these structural changes in biomolecules play important role for molecular recognitions? 

So then i use molecular dynamic simulations, which is again got a nobel Prize. So this technique got a Nobel Prize, and Michael Levitt is one of the main key player behind this. So what is the molecule dynamics? It mimics similar atmosphere into the computer. So you can tune the pH, you can tune the temperature, you can tune the ionic strength, you can tune lot of similar environment you can create in a computer. When you have a similar environment, what you can understand, if this is the molecule, what will be the time dependent, it's a trajectory, time dependent behavior. Then I came to know that the molecular dynamics simulation could play a very important role and hence during my PhD I used quantum mechanics and molecular dynamics simulations. 

So this is the journey with respect to the PhD and then I joined IIT Bombay.

Mr. Ravindra: Amazing. So I think it's a really breath-taking journey, I must say. But you mentioned something about computational chemistry. So I would like to know more about that. 

Dr. Bajarang: Yes, computational chemistry. Now, my second goal came into the picture. Then I was looking for my post-doctoral journey and professional journeys. So at the time in 2014, I joined I did PhD from 2007 to 2013 and then I joined 2014 in IIT Bombay. So I joined Professor Ambrish Kaur's lab as a computational biochemist. So where I want to learn how there are a lot of anti-cancer drugs, like Texol, Vinplastine, Combrastatin, Irubilin, these are the drugs that are there. 

But still, this drug shows a resistance pattern. So if you look at the resistance pattern, these are very important drugs, but there there are some limitations of drugs and resistance pattern. So the first job of mine is that what is the reasons behind this drug resistance. So we did a first study how different tubulin isotype; that is a component of a microtubule show the differential affinity. So for that I need to do a computational chemistry. So what computational chemistry is here? So I would like to make a model. do a molecular docking, computational docking studies, and then we perform a lot of molecular dynamic simulations. So this gives the realistic view that if these different tubulin isotypes or microtubules components are there, how one drug binds to different way. 

So where we use this kind of techniques and we came to know the first time the colchicine or venblastine or utiboline or indianosine have differential affinities. And it is reported in one of the study by Professor David Odey. He mentioned that this is the first competition study which mentioned about the differential affinity of the protein for one drug molecule. And this we take to next level. 

When I become a professor at NMIMS and what we are actually doing, we know that this is the reason. So we have to find out the new drug molecule where computational chemistry is there. So what currently we are doing, we are trying to find out the potential drug molecules. So if you look at the online drug library, like Zinc is there, Drug Bank is there, a lot of online drug libraries are, repositories are there, where scientists synthesize the drug, put into the databases. 

We take that drug, we have a smile code. We have a one-dimensional code. We have a 3D structures are there. Now, we can apply a computational chemistry that what is computational chemistry like a DNA property adsorption, distribution, metabolism, excretion, toxicity study. These are the potential parameters when computational chemistry. We know that. So, we can find out the properties of the drug molecule which are matching to the earlier drug molecules. So, here we have a drug library and we know that these are the potential drug which is the current level in the market. Now we have to find out the similar drug molecule from the library. So here we are using computational chemistry. We find out the ADMT profile. We find out the pharmacokinetics properties of that using machine learning approach. 

So we use the machine learning. In machine learning, we find out these are the active drug molecule which is earlier used for the potential target. And these are the possible drug library in which we have to find out the potential drug molecules. So here we use a machine learning. 

So what is machine learning? It is there are, in machine and there are two types. One is a supervisor and unsupervised. So what we are using, we are using supervisor. What is supervisor? In which we know that this is active drug molecule but doesn't show the affinity right now. And we have to find out the similar drug molecule from the library. So what we can do, we use a, this is a training set, and this is a test set. 

So from the test set, we have to find out the similar molecule from the training set. So that's why we use a, like, different AdaBoost algorithm, machine learning, then NLPs, there are multiple algorithms are there. Random forest. There are multiple organisms so we use the statistical algorithms using machine learning and filter the, out of 1 million, 2 million, we find out only 10, 15 drug molecules, which is have a similar property the earlier drug molecules. which is a similar molecules. And then we perform a docking, then molecular dynamic simulations, and we came to market this out of one million, these are the only five molecules have potential affinity so uh i will again come back to my journey where my postdoc, after postdoc what i did. 

But I want to connect this part first. 

So how to learn machine learning? So if any undergraduate students are there or PG students are there, how they can learn this? This is a very simple technique. If you know the statistics, you can do better machine learning. So if you have mathematic knowledge, 11th and 12th, statistical knowledge, you can do better machine learning because these are the advanced tools of statistics. So what is machine learning? Where you have to use online libraries are there and from that you have to screen. But how to use that? What kind of knowledge you require? So some computational knowledge you require like Python, R, these are the some libraries are there. So if you spend one to two months on that, ultimately you will get a better idea that how to use that tool and then once you are aware about the tool, you can use the online tutorial and then you can train your models. 

It's not a big deal. It's a simple way. Only you have to spend some time on that and you will get better results from that. 

Mr. Ravindra: Certainly, that’s wonderful. I'm coming back to the computational chemistry part in industry right now it's It's being a buzz word. So there are trends which are organization are following that the computational chemistry is the center of all this stuff. So what do you think, what is the future of this? 

Dr. Bajarang: Yeah, future it's a very good question actually. So what is the future of computational chemistry? So you know that you are synthesizing 10, 20 molecules, 30 molecules. And you came to know that out of 30 molecules, only one molecule is effective. 

So your efforts related to the synthesizing that is going into the, you have to spend a lot of money on that. Suppose if you make a library, 1D or 2D library on the paper, or 3D library, and use the computational tools. Like computational chemistry, what exactly computational chemistry means? So computational, you can extrapolate the properties. Like dipole movement you can find out. You can find out homo, lumo, energy gap. Higher occupied molecular orbitals, lower unoccupied molecular orbitals. So that gives the idea that whether this molecule can share electron or not. Can accept electron because if any drug molecule that have a properties….

Mr. Ravindra: So I want to come back to computational chemistry. I really wanted to understand what is the future of computational chemistry and what are the applications of it? 

Dr. Bajarang: Yeah, it's a very good question. I think I have a lot of information for this. So what is computational chemistry? So generally, industries are actually looking for computational chemistry as one of the important tools. Because why? Because they want to save the money. Every industry needs to save the money. If you want to save the money, what is the requirement? You know there are some predefined areas, predefined drug molecules are there. So how to do that? So generally if you have a suppose small molecule, a purine molecule, if you experimentally, if you write down what are the different kind of libraries are there, what are the different derivatives are there, if you make derivatives on the paper, and when you convert into the 2D conformation or 3D conformation, what you get? You get a similar kind of molecules, which is crystallographically also same, same bond distance, same bond angle. 

So if you have this kind of a molecules are there, you can use that molecule for computational chemistry. What is computational chemistry? The computational chemistry gives the extrapolate the properties of that particular chemical compound. What are the properties? Like dipole moment is there, HOMO and LUMO is there. Homo, higher occupied molecular orbitals. Lumo, lower unoccupied molecular orbitals are there. So if you have these higher end LUMOs, you can get the band gap energy. So band gap energy is very important because it gives the idea that whether that molecule donate the electron or accept the electron. 

If the molecule have that properties, then it will be a good property. Another area where you can think about computational chemistry is that ADMT properties. You can find out the pharmacokinetics property, pharmacodynamics properties, that property you going to find out? So along with that, you can get to know what kind of heat of formations are there. So computational chemistry is a tool which is based upon quantum mechanics. Okay, which is based upon the properties of the molecule. So suppose if you have a string of molecule, one day that code, so you can find out how many electrons are, how many carbon atoms, nitrogen atom, oxygen atoms are there. What is the molecular weight of that? If the molecular weight is 500, there is no use. There is use, but the molecular weight should be in that range. So molecular weight lipophilicities blood bag variant across areas, can be possible with their public computational chemistries. 

And once you have a library, so you can use that library against that so you can convert 1D to 3D and this 3D compound you can do for docking. So for docking with target molecules. So target molecules are the molecule which are the potential one which can be play important role in drug but this is condition. So if you target that molecule so what is the condition is that so you can get the out of this library 10 or 20 which molecule is a better molecule. 

And then you can go for the molecule dynamic simulations and get the binding affinity of that. 

so computational chemistry could play a very important role in future for finding a potential drug molecule. 

So we have a one or two article on that. Recently we are working, we work on SARS-CoV-2 main proteases. We find out the anti-diabetic molecule could be potential use for the SARS-CoV-2 because the anti-diabetic molecules are in case of SARS-CoV-2, most of the people, those are diabetic, they are more prone to the SARS-CoV-2. So we find out if we use this drug molecule, it will be potential to the main protease of the SARS-CoV-2. Also, similarly, we are currently working on the microtubule searing enzymes where we are trying to find out the purine-like molecules. So we screen out 2.76 million molecules, purine-type molecules. And we're using computational biology structural approach. 

We find out only 5 molecules which are effective molecules. So, these are the purine and which could play important role in anti-cancer proliferations. So, it can bind to the cationine and then it will be hampers the activities. 

Mr. Ravindra: So, out of the curiosity, I wanted to understand is there is computational chemistry has any application biosimilar kind of products? 

Dr. Bajarang: Yeah, there is a biosimilar is also similar. So, there is a lot of application in computational chemistry. 

Mr. Ravindra: Interesting. Yeah. So, I think this is a very niche area. So, I have a very interesting question here. So, coming from a very small town of Maharashtra called Kolhapur and being a professor to one of the very renowned universities today, NMIMS, right? And getting into the very world-trendy area called AIM in Pharma, what do you think? What is the inspiration of yours and what is the one advice you would like to give to the people or students who are coming to this domain? 

Dr. Bajarang: I think you touched my heart because I will tell you that I am coming from Sangli, Atpadi, which is a taluka in Sangli district. So, Atpadi is, you know, the drought means Atpadi, that is relations. So, we don't have water. and I will tell you one story. I think in general, this is the first time i'm talking about my story. So I failed two times in 10th. I failed two times in 10th. So when I failed, then I realized that my friends are going to college and I was working in pharmaceutical shop and all, cloth shops. 

And then I realized that I have to study. I have to go to the next level. I failed in mathematics because we don't have good teachers there. Might be I'm not that much of attentive that I can spend time on studying because nobody is from my family. Those are actually go to the school. I'm the only who did my MSc, PhD and right now I'm in NMIMS. So I realized that after the 10th, I have to pass. Somehow I have to pass. And there are some teachers who help me, which are private class teachers, but they help me. And I somehow pass in 10th with the mathematics. Then I realized that why should I take admission in Arts because I failed. So generally most of the time it happens that after the fail you can go to the Arts. 

Then I feel a form for art. Then I attended 2-3 regular college. Then I realized that nothing is there. So nothing is important. So regular college is there. Then I go to the commerce. So I fill a form for commerce. Then I attended 3-4 days. Then I realized that so much has happened. What is left in life? Nothing happens after 10th. Then I go for the science. I say, if so much has happened so far, even if nothing happens after that, it will be fine. We will say that this is the 10th fail, so if something happens after that, there is no impact on my career. But I like science. I love the definition, like what is biology? It is the study of plant and animal that gives the life science. 

So I found the interesting figures and tables. I found it very interesting. Then slowly I was learning and in 12th I got a 14% mark. Then my slowly, when i did my BSc in Chemistry, I got 80 mark and then um then in uh after that i got admission in msc biochemistry in Shivaji University Kolhapur then my career started. See, for long term, you should do not, Do not focus on your, sometimes do not disappoint with small failures in life. You have to jump to the next level. And then whenever I see my two-mark list, I realize that yes, I have two-mark list. Sometimes I say proudly I have my two-mark list. But when I have a MSc and PhD degree, then I realize that I should have to go to PhD. 

Then I got a PhD in 2007- 2013, I did a PhD in computational chemistry. And after that, I joined IIT Bombay. where I published around 11 papers from IIT Bombay. That is one of the best performance from my side to the IIT Bombay. So then that one of the paper is with Rahul Purwar and the company's Immuno-Act that is on Molecular Cancer Therapy that changed my entire life. That paper is on Cancer Therapy, Immuno-Therapy. That is CAR-T Cell Therapy. I will talk about that because currently I am working on that area only. 

And then in 2019, the COVID is there. Again, I don't have a job and I'm looking for the job. Then in 2021, I joined NMIMS. And from NMIMS, within two and three years, with collaborations and independent laboratory, I published around 18 papers.

So out of that 18 papers, I got the first paper, like a very important paper is in nature, so nature with impact factor 17.69. So that is one of the best given from my side today in my career now now i have a two PhD student and four to five project staff is there. They are working on this machine learning, drug discovery, and car t cell therapies. 

Mr. Ravindra: I think that's really a journey, I can say. This will certainly inspire our readers, audience, like anything. So, failing 10th standard twice, coming from a very small town in Sangli, being a professor and publishing more than... 

Dr. Bajarang: I have 45 papers right now. I'm reaching to 50 very close. 

Mr. Ravindra: That's really wonderful. I think it's... It was really great to have you here and to have a conversation with you. We are really looking forward to this interview. Thank you very much.