Q&A with our new Editor-in-Chief

What is your research background?
I was trained as a computational chemist for my PhD and then obtained additional broad training in molecular biophysics and pharmacology through postdoc research studies. My current research at UNC Chapel Hill is focused on biomolecular modelling, cellular signalling and AI-driven drug discovery. I develop novel theoretical and computational methods and Deep Learning techniques, which speed up biomolecular simulations by orders of magnitude. I apply these methods for unprecedented simulations of biomolecular dynamics such as protein folding, drug binding and protein-peptide/protein/nucleic acid interactions. In collaboration with leading experimentalists, my lab combines complementary simulations and experiments to uncover functional mechanisms and design novel drug molecules of important biomolecules, including G-protein-coupled receptors (GPCRs), membrane-embedded proteases, RNA-binding proteins, and RNA.

What is your favourite thing about research?
I really enjoy the excitement when everything comes together, providing a beautiful solution to a tough problem you and probably many others have been working on for long time. It could involve initial physical/chemical intuition, theoretical mathematical derivations, hard-core coding in a software program, many iterations of code testing and debugging, very nice demonstration of the new method on well-known model systems, and finally telling an intriguing story of the whole thing to experts and colleagues in the field. I would feel joyful, and all the hard research work will be worthy if our developed methods and tools are useful for different applications of people’s interests.

What has been your biggest challenge and your greatest achievement in your career so far?
It was very hard for me to find a faculty job during postdoc. I had to balance between faculty interviews, working on exciting research projects and raising a family. For research, I had been working on accelerated Molecular Dynamics (aMD) simulations of various systems in the McCammon lab at UCSD, with some successes on enhanced sampling of protein folding, ligand binding and GPCR activation. But the boost potential energies in these simulations were too noisy for us to reweight the simulations and recover the original system free energy profiles, even as I tried all sorts of reweighting algorithms. Fortunately, I realized if the boost potential of these simulations follows a Gaussian distribution, we could properly reweight the simulations using so called “Gaussian approximation”. With that in mind, plus a number of enhanced sampling principles, I implemented a different formula to generate the boost potential in a new method, which we called Gaussian aMD or GaMD. I was excited that GaMD provides both unconstrained enhanced sampling and accurate free energy calculations even for very large biomolecules, and published the paper in 2015. Coincidently, my son was also born in the same year. I’m very happy to see them “growing” over the years. Some of our recently developed “Selective GaMD” algorithms are able to predict binding thermodynamics and kinetics of small molecules, peptides and proteins from only microsecond simulations (very efficient and accurate). The binding free energies and kinetic rates are critical parameters for therapeutic design of drugs, peptides and antibodies.

What are you most looking forward to in your role as Editor-in-Chief?
Drug discovery is extremely expensive and time consuming for both academia and pharmaceutical industry. There are various very difficult research problems at different stages of drug discovery. As Editor-in-Chief, I’m very much looking forward to working with colleagues and experts around the world to promote research in the drug design and discovery, which will eventually benefit human health. I aim to build an excellent team of Associate Editors and Editorial Board Members, along with dedicated staff members. Together, we are strongly committed to high-quality editing and reviewing services. Our published research advances will hopefully accelerate the timeline and significantly reduce the cost of drug discovery.

Why should researchers submit their work to npj Drug Discovery?
The npj Drug Discovery is a unique journal dedicated to publishing peer-reviewed and high-impact research in all aspects of drug design and discovery using experimental and computational techniques. It covers research concerned with all drug targets, including proteins, nucleic acids and bimolecular complexes, and various therapeutics such as small molecules, peptides, antibodies, gene therapies, etc. The research work could involve any stage of drug development, including drug target identification, library screening, lead optimization, animal models and clinical trials. Our editorial team is led by experts in computational modeling, medicinal chemistry, pharmacology, and experimental therapeutics. For the first two years of publishing in the journal we also have generous Article Processing Charge waivers for authors that need them. We are committed to fair evaluations, fast decisions and preferably constructive suggestions and comments that can help the authors to publish their best exciting work in a timely manner. Our goal is to make the journal a leading platform for worldwide researchers to exchange the latest research in drug discovery.