How did you get interested in Medicinal Chemistry?
I have a MSc in Pharmaceutical Biotechnologies from the University of Bologna. When attending the final year in the program, thanks to some compelling lectures, I got interested in the subject of drug discovery. However, I have to say, it was computational molecular modelling that sold me the discipline.
Where and when did you obtain your PhD diploma?
After obtaining my masters degree, I went straight to graduate school, which I attended in the QSAR and Molecular Modeling Group of Prof. Recanatini at the Department of Pharmaceutical Sciences (now FaBit), University of Bologna.
What was the topic of your PhD project?
I worked on a clustering algorithm written in Python – which we eventually published under the name of AClAP - that applied unsupervised learning to the outcome of docking simulations. I guess I could rightfully claim that we did machine learning way before It was cool!
Where did you have your postdoc position?
After my PhD I joined the group of Prof. Abagyan at The Scripps Research Institute in La Jolla, CA – USA, where I spent over two years working on receptor flexibility in protein ligand docking. In 2008, I went back to Italy to join Prof. Cavalli’s computational chemistry group at the Dept. of Drug Discovery and Development (D3) at the Italian Institute of Technology (IIT, Genova) then newly founded by Prof. Piomelli. I ended up becoming a team leader at D3, eventually spending eight years in Genova.
Where do you work at the moment and what is your current position?
After a few years working in industry, I am now a senior lecturer in computational medicinal chemistry at the School of Pharmacy at the University of Birmingham and PI of the Computer-assisted Molecular Design group (CAMD lab).
What are your current research interests?
I always try to develop ideas and projects in which computational modelling is immediately translated into new and, possibly, better molecules. After joining the University of Birmingham, I kept on working on some of my long-standing interests, namely computational polypharmacology and membrane proteins. I have recently started working on modelling approaches geared toward protein loop mimetics in cancer treatment. My main research line revolves around developing multi-target compounds for the treatment of nicotine addiction.
What do you like most in your job?
I really, really get my kicks from experimental data confirming computational predictions. I believe that the discipline of [molecular] modelling should be approached focusing on the literal meaning of the word “modelling”: always bearing in mind that what we do creates a model that attempts to explain collected evidence. This model. Should be used to design future experiments. I mean, it is the scientific method, didn’t really change that much in the past 400 years. Preliminary evidence suggested that Aristoteles might have been wrong, Galileo conceived a model of how objects of different weight would actually behave. Then, he did not leave it at that. Based on his model, he created a hypothesis about the outcome of an actual experiment and, presto, he started dropping gravi from the leaning tower of Pisa.
What kind of tasks your job includes?
Currently, I am on a so-called three-pillars contract: my duties include research, teaching and administrative work. When I am not busy in the CAMD lab at the School of Pharmacy in Birmingham, I teach chemistry in our MPharm and I am academic director of business engagement for the college of medical and dental sciences (MDS). This last role is particularly interesting: my job, together with my clinical counterpart and the manager of the business engagement team, is to develop the framework for scientific collaborations between companies and the PIs at MDS, identifying opportunities for overlap between scientific interests.
What kind of skills your job requires?
One, often underrated, skill is the ability to deploy a huge amount of work. Like, a lot.
I understand how celebrating quality over quantity sounds glamorous, appeals to the stereotyped idea of the egghead working on brilliant intuitions. Don’t get me wrong: quality is fundamental. Sharp minds and, sometime, the genuine spark of genius are instrumental to the progress of science. Yet, I just can’t see much being accomplished without the often-belittled contribution of long hours spent in the lab on apparently (?) tedious and repetitive tasks, weekends and late nights spent coding, scientific papers read on your smartphone while commuting to work, etc.
And junk food, of course. The ability to survive on junk food and to endure, actually enjoy, the awful coffee from vending machines must rank very high on the list of skills this job requires. As soon as they’ll start working in research, these fully-organic, low-carb eating Gen-Zs are in for a big surprise.
What do you consider your biggest achievement in your scientific career?
My scientific career has always been very much influenced by the work of Henry Chesbrough on open innovation. I have always tried to follow a path that would keep me and what I do at the interface between academia and business. Having been able to obtain a master in healthcare management and to co-found a start-up while doing full-time science is something I am quite proud of.
What are the features of a successful PhD student or postdoc?
Well, I believe that the first element to understand is actually how PhD and post-doc should be regarded as two very different things. Namely, a successful PhD should be focused on a challenging project, something with a genuine chance to improve on the state-of-the-art while learning a wide range of transferable skills including the most valuable one: how to tackle a complex issue that requires stress management and long-term planning. Conversely, a postdoctoral appointment shouldn’t be a second, possibly shorter PhD project but the beginning of a path toward an independent position.
What is the most embarrassing thing you did in the lab while doing experiments?
One time, together with a colleague of mine (F.C., if you are reading this, forgive me for betraying our secret!) we tried to replace the NIC - the wired network interface controller - in one of the workstations. We opened the machine up, took the dead component out, placed the new one in with religious care – upon our recommendation, our PI has agreed to spend an outrageous amount of money on what was back then considered a straight-from-the-future piece of hardware: the Gigabit Ethernet controller – and… it didn’t work.
No matter what we tried: sweat, anger, reboots, ifconfig, obscure technical forums and our nerd mojo combined, we were going nowhere. Dead in the water. After two days, we were about to give up and go back to the boss’ office, tail between our legs, admitting that the NIC from the future had bested us both when… “F.C., have you actually plugged in the ethernet cable?” “Me? I thought you did…”
Which scientist do you admire the most and why?
This is an easy one: Laura Bassi.
First woman ever to become a university professor. She corresponded with some of the sharpest minds of her time, including Voltaire, Bonnet, Volta. She mentored Spallanzani. The establishment really couldn’t stand her, but she was so good that eventually she couldn’t be ignored. Well, that, and the fact that her uncle was pope Benedict XIV. That, for sure, did help.
Did you experience any unfair situations during your scientific career?
Unfair? No.
I mean, don’t get me wrong. Like many others, I have had my share of submissions torpedoed by biased reviewers, I have been short-listed for positions belonging to the internal candidate, I’ve seen my papers go uncited by people who were obviously familiar with their contents. But these aren’t truly unfair situations, are they? Mildly annoying, maybe, but just minor inconveniences in the grand scale of things. I’ve never had to endure discrimination or prejudice. I’ve had pleasure to have always worked for diverse, cosmopolitan and welcoming institutions. And I consider myself very lucky for that.
Which paper of yours you are the proudest of and why?
The J Med Chem paper which I published in 2009 with Irina Kufareva, Max Totrov and Ruben Abagyan on the approach called 4D docking developed when I was a postdoc in Ruben’s lab, is a paper I am very much fond of. It was (still is, even by today’s standards) a massive validation study carried out on a very diverse benchmark. Despite being computationally intensive, it conveyed a very clear message to the broad med chem community and not just to the specialists. Furthermore, it was accepted for publication less than a year after that famous editorial by Prof. Bajorath aimed at raising the bar for computational contributions on JMC.
What would you guess to be the next major breakthrough in medicinal chemistry?
Do you know that Robert Metcalfe predicted that by 1996 the Internet would have been gone and forgotten? My point is: predicting the future is a tricky business and the risk of being proven monumentally wrong 10/20/30 years from now is real. However, I’ll try an educated guess, extrapolating from the present to a very near future: a prospectively validated multi-disciplinary pipeline that, combining elements of cryo-EM, machine learning, molecular dynamics, fragment-based drug discovery and some clever analytical technique such as native protein mass spectrometry will be able to systematically identify ligandable allosteric pockets.
