Dr. Sabrina Maniscalo, CEO and Co-Founder, Algorithmiq.  Algorithmiq partners with IBM and using quantum computing to bring efficiencies to drug discovery.

Dr. Sabrina Maniscalo, CEO and Co-Founder, Algorithmiq

We wrote earlier about Algorithmiq and its partnership with IBM. Algorithmiq’s ambition is to use quantum computing to bring efficiencies to drug discovery and carve out a break out market space around Quantum Biotech.

This week, Algorithmiq also launched its quantum computing based drug discovery platform Aurora.

Please see below for a fascinating exchange with Sabrina Maniscalo and Algorithmiq’s path to solving an important problem using quantum computing.

What’s your background? How did you end up becoming an entrepreneur?

I am Sabrina Maniscalco, a Sicilian living in Finland, and I’m a professor of quantum information, computing, and logic at the University of Helsinki. I’m also the CEO and co- founder of Algorithmiq. I’ve been working in quantum science and technology for over 20 years.

My expertise lies in the very subtle interactions that quantum systems have with everything around them. There is a very technical term called open quantum systems, and there’s a theory that describes them. It is really about why quantum systems are so fragile and delicate, which is the reason why we have errors in today’s quantum computers. It is one of the main enemies of existing devices because we want to make them as perfect as possible in order to really operate at a large scale.

It was the right opportunity to start Algorithmiq as I had assembled an amazing team and we saw the opportunity to commercialize our IP after a fruitful scientific collaboration with the team of quantum physicists of IBM Zurich.

What problem are you trying to solve at Algorithmiq with quantum computing? How is joining IBM Quantum Network an advantage for Algorithmiq?

We are focused on leveraging the power of quantum computing so that new drugs can be explored and eventually brought to market, and cost-effectively, leading to precise medical treatments. On average, it currently takes around a decade and $1 billion for a new drug to get to market.

The collaboration we have entered brings together IBM’s world-leading hardware, software and quantum applications expertise with Algorithmiq’s cutting-edge algorithm developers, to explore ways to dramatically cut the time and cost of drug discovery and development. The work will also contribute to Qiskit, an open-source SDK for quantum computers, with the aim of promoting and developing this nascent ecosystem.

Being part of IBM Quantum Network is of course a privilege because of the support and the networking that IBM’s ecosystem offers but our collaboration will go beyond this as we hope to achieve an important milestone for the wider quantum community, that of paving the way to proving first quantum advantage for chemistry.

Drug discovery is a complex process because of various permutations and combinations of chemicals and its relevance to addressing specific medical conditions. I read elsewhere and loved your comment about 1063 molecules existing in the world that can impact the creation of a new drug. How does Quantum computers bring an advantage that classical computers cannot in this context?

With classical computers it is very difficult to reliably predict the binding affinity of an unknown drug to the protein. Conventional methods must rely on some approximations that limits their predictive power. There are also AI methods that learn from known molecules and can extrapolate somewhat. However, the whole space is so much larger.

That’s why first principle calculations are essential. The quantum computer does not know whether a molecule is known or not. It is just a molecule and therefore it behaves according to laws of quantum mechanics. Molecules are quantum systems (and nature is fundamentally quantum), therefore this is a quantum problem that requires a quantum solution.

Classical computers simply do not have the computational power to explore the vast space of unexplored chemical compounds – this would require a memory larger than the number of atoms in the entire Universe. Quantum systems of increasing complexity – like molecules – are impossible to study precisely by means of any conventional computers, this creates major roadblocks in many fields of science. Drug Development and Discovery is the most impactful example.

Despite a tenfold increase in spending for research, the number of new drugs brought to market remains approximately the same. The reason is that existing approaches are oversimplifying the cell biology. We cannot predict with high enough accuracy the binding of potential drug molecules to the proteins in our body responsible for a given disease. As a consequence, 90% of drugs are not effective for half of the people they treat. But there is hope as Quantum computers possess the very same properties that make drug molecules hard to study on any conventional computer, so they are by definition apt to simulate them.

There is a lot of talk about quantum computing being theoretical and practical applications being further out. With Algorithmiq, what would your solution that look like?

Fault-tolerant computers are indeed 10 to 20 years out and continues to be the aim for the industry. However, current near-term devices (the smaller and error-prone computers available today), can be ‘useful’ now with the skillful use of algorithms and this is what we focus on at Algorithmiq.

Algorithmiq’s USP comes from the way that we measure the output of the computer using what we call informationally complete data. We have discovered and patented a method to combine the results of a quantum computation with the most powerful classical algorithms in a way that is efficient, accurate, and scalable. This makes Aurora (Algorithmiq’s drug-discovery platform) the only platform able to use today’s quantum computers for problems of relevance to drug discovery.

The interesting thing about technology is that it can disintermediate other industries and sometimes the line between technology developer and solution provider becomes blurry. How does Algorithmiq work with pharma industry today? Would Algorithmiq morph into a full-fledged pharma company at some point?

This is a very interesting point. Right now, we are focused on collaborative work with pharma companies where we aim to establish with them the areas which would most benefit from having a ‘quantum boost’. This is the first most useful step where we can help to unlock value for our pharma partners.

Our long-term vision is eventually to become a quantum biotech where we can own our own drug discovery programs.

What are your short-term challenges? And what are the greatest rewards awaiting Algorithmiq?

Our short-term challenges are in the complexity of the problems we have to solve every day. In research there is always a lot of unknown, with very low lows and very high highs when we get a breakthrough (and we have had a couple of these over the last few months). When these happen, they can really be game-changing as we are dealing with ‘so called – exponential technology.’

But the greatest reward is the team that we have built. We have a world class, multi-disciplinary team of experts in the field and together we have amassed over 300+ peer reviewed publications. The effort we put in hiring the best people at the very start is not only a game-changer in an industry where so much of the ‘real’ talent is scarce but it continues to provide positive ripple effects, which we reap every day.

Who are your investors?

We raised a seed round in February 2022, a $4M seed round backed by investment from CEO & Founders of Tiger Global, K5 Global and numerous angel investors. Alongside Co- Founder and CEO Professor Sabrina Maniscalco, Algorithmiq’s Board consists of Jorma Ollila, former CEO and Chairman of Nokia, Haakon Overli, founding General Partner at Dawn Capital, and Co-Founder Dr Jussi Westergren, early investor in Deepmind.

We currently have an open Series A round which is gathering some really strong interest from world leading funds and VCs with an anticipated close for end of the year.

Finally, what’s your advice to people interested in quantum computing? What advice do you have for entrepreneurs in quantum computing?

Hire your team well and do not underestimate the nascency of the industry when addressing the general public. With any new promising technology that has the potential to be truly disruptive, the recent hype and the lack of benchmarking across the technology creates difficulty (for investors, policy makers and the general public) to truly assess the value or potential of what a company is able to provide in this space. However, quantum will be disruptive and for those interested in joining the space, there are some fascinating developments.

November 2022