[Interview] This Vancouver-based Startup Plans To Boost Drug Design With AI

The organizing principle of Variational AI is that the exponentially growing cost of drug discovery can only be halted if the pharmaceutical industry shifts the paradigm by which it searches the space of molecules. Variational AI has developed a machine learning algorithm that organizes the full space of 10^60 drug-like molecules based upon their pharmacological properties rather than their chemical structure, enabling state-of-the-art QSAR and transformative multi-property inverse QSAR/QSPR. They use sophisticated deep learning to build a nonlinear ligand-based pharmacophore, which implicitly accounts for induced fit, solvation, entropic effects, and multiple binding domains. Just as a chess grandmaster develops an intuition for good moves by studying millions of games from early childhood, their algorithms learn pharmacological intuition from vast volumes of raw experimental data that would overwhelm any human. Within their property-based search space, they simultaneously optimize pharmacological activity, synthesizability, ADME, and toxicity, directly producing highly novel lead candidates. By integrating multiple assays for each pharmacological property across many datasets, they avoid overfitting to any single approximate or noisy assay and increase the probability that your final drug candidate successfully progresses through clinical testing.

Canada has been emerging as a global hub for artificial intelligence technologies, and it also seems to be a suitable place for building AI-driven drug discovery startups. Recently, I made a brief review of top AI-augmented drug design companies in Toronto, a city of biotech entrepreneurs and technology innovators, and found that 13 top-notch companies are already showing considerable achievements in the pharmaceutical space -- having raised collectively more than $60 million in early-stage venture funding and grants over the last several years. But Toronto is not the only “sweet spot” in Canada to build a successful company in the pharmaceutical space, especially when it comes to computation and artificial intelligence. Variational AI is one recent example of a company in Vancouver -- another promising destination for AI entrepreneurs, thanks to its rooted academic background, strength in applied AI, and rising support for the entrepreneurial community by the government of British Columbia. According to the AI Network of British Columbia (AInBC), Vancouver has 150+ AI/machine learning start-ups working across a range of application areas.

I have asked several questions to Handol Kim, Co-Founder, and CEO at Variational AI, to have a glimpse into the plans and aspirations of the newly formed company, and find out how Variational AI is going to compete with a growing number of AI-driven drug discovery vendors out there. Below is a brief interview:

Handol Kim, Co-founder, CEO, Variational AI

Handol, at what stage is your drug design platform? Do you already have some proof of concept and how does it compare to existing industry benchmarks (if any)? 

The standard benchmark tasks used to evaluate AI algorithms for molecular property optimization are a maximization of the Crippen octanol-water partition coefficient (logP) and the quantitative estimate of drug-likeness (QED). LogP and QED are useful heuristics for optimizing ADME properties and can be evaluated quickly and reproducibly, so the quality of optimized molecules can be compared consistently between algorithms. On these measures, we produce molecules up to 3.3 times better than competitors. AI molecular property prediction is commonly benchmarked using the NIH’s Tox21 dataset, comprising in vitro measurements of twelve toxicity pathways across ~8000 molecules. Again, we achieve the best performance in the world predicting the properties of the held-out test set designated for this benchmark.

Moreover, our algorithm only requires a fixed training set of molecules on which properties, such as binding affinity or toxicity, have been measured. In practice, this training set will include the collected experimental data from a drug development pipeline, such as high-throughput screening, in vitro measurements of ADME and toxicity, and in vivo experiments on a smaller subset of molecules. It is only by leveraging this experimental data that machine learning can avoid the approximations, biases, and false confidence of structure-based drug design.