NYAGIM is excited to welcome members back for our first in-person event since 2019! Dr Stephen MacKinnon from Cyclica will present: "Proteome-Scale Drug-Target Interaction Predictions: Approaches & Applications" The location is JLABS @ NYC (101 6th Ave 3rd floor) & the talk will be followed by a food & drink reception. Please note that we are strictly limited to 70 attendees & we are not able to broadcast this event online so it will be an in-person event only. ABSTRACT Drug Target Interaction (DTI) predictions have recently gained widespread popularity with advances in machine learning & publicly available bioassay datasets, such as BindingDB, DrugBank, PubChem & ChEMBL. Machine learning based strategies frame DTI predictions as a discriminative supervised learning problem, whereby combined pairs of features derived from the ligand (drug) & protein (target) are classified as a binding (positive) or non-binding pair (negative).

Research into the therapeutic potential of psychedelic drugs was pioneered by psychiatrists way back in the 1950s, but the emergence of advanced technologies in pharma appears to have breathed new life into the field. As interest in the psychedelics market gains stream, a number of drug companies are now employing artificial intelligence (AI) methods in their search for new psychedelic compounds to treat a range of mental and physical conditions. One in four people in the UK will experience some kind of mental health problem every year, and figures are almost identical in the US. Despite this, treatments for psychological conditions are relatively limited – and for many patients, the drugs that are available come with side effects that negatively impact their quality of life. Psychedelics are hallucinogenic drugs that alter a person's perception and mood and affect their thought processes.

Deep-learning technology allows Toronto-based biotech firm Cyclica to help researchers, companies and governments do drug discovery in record time--including in the COVID-19 crisis. On January 27, 2020, there were still fewer than 1,000 confirmed reported cases of SARS-COV2 in the world, confined mostly to China, though the first positive tests had also appeared in the U.S. state of Washington and would soon follow in Northern Italy. On that same day, the leadership team of Cyclica--a data-driven, drug discovery biotech company based in Toronto--met to discuss how it might get involved. Within a week, discussions had commenced with China's Institute of Materia Medica about sharing Cyclica's AI-enabled platform, Polypharm DB, platform to begin isolating potential therapies through machine learning. On March 5, both sides formalized their partnership, and days later, anticipating the coming quest for inoculations and remedies, Cyclica made the platform available pro bono to any researchers working on treatments for what would soon be widely known as COVID-19.

So far, the pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately its business model has been under significant pressure since the return on R&D investment has dropped to its lowest level in decades (lack of innovation amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility) and its public reputation in US and around the world (anti vaccine movement in Europe) is worse than ever. This worrisome mix of little growth potential and low reputation is the main reason why investors are increasingly worried, not to mention that the current drug development process needs a big dose of digital innovation to deal with its messy data. As a matter of fact, Stefan Oelrich member of the Board Management of Bayer AG, President Pharmaceuticals, wrote in an article -- that the title perfectly summarises the AI pharma situation "Artificial Intelligence - When we Suddenly Know What we Don't Know" -- the following: "As we open the first doors in this unknown land we start to discover how much more is out there for our entire pharmaceutical value chain spanning from research to product supply. I expect AI to help us know what we have not known so far. Artificial Intelligence will become instrumental in our search for new medicines to better serve patients around the world as we leverage Science For A Better Life".

Naheed Kurji is the President and CEO of Cyclica, a Toronto-based biotechnology company that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Cyclica leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Can you discuss in what way AI is used in this process? Technology has played a critical role in drug discovery dating back to the '80s. However, the drug discovery and development process is still very inefficient, time consuming and expensive, costing more than 2 billion dollars over 12 years.

Cyclica has designed, patented, and optimized our Ligand Design technology, which can uniquely de novo design chemical entities across a panel of desirable targets while avoiding undesirable anti-targets. Driven by a metaheuristic genetic algorithm coupled with a novel AI technology that we have built internally at Cyclica called POEM (patents filed Sept 2018), Ligand Design begins by fragmenting seed molecules and derivatizing them with preferred fragment libraries to explore synthetically accessible chemical space. Ligand Design then selects amongst these hypothetical molecules for those with desirable physicochemical and pharmacokinetic properties to proceed to the next step - this is based on our internally developed ADMET-Prediction technology. Ligand Design then computes polypharmacological profiles, selects those with preferred profiles, and then initiates another cycle of Ligand Design. This process continues until a set of molecules with desirable properties are fashioned.

THERE ARE MANY REASONS that promising drugs wash out during pharmaceutical development, and one of them is cytochrome P450. A set of enzymes mostly produced in the liver, CYP450, as it is commonly called, is involved in breaking down chemicals and preventing them from building up to dangerous levels in the bloodstream. Many experimental drugs, it turns out, inhibit the production of CYP450--a vexing side effect that can render such a drug toxic in humans. Drug companies have long relied on conventional tools to try to predict whether a drug candidate will inhibit CYP450 in patients, such as by conducting chemical analyses in test tubes, looking at CYP450 interactions with better-understood drugs that have chemical similarities, and running tests on mice. But their predictions are wrong about a third of the time.

Chief Growth Officer at BenchSci, helping more scientists plan successful experiments with AI-driven reagent intelligence. Naheed Kurji was pitching his AI drug discovery startup, Cyclica. The audience was a large group of scientists. It included senior scientists and heads of computational drug discovery and chemistry. These were the right people. Kurji hit the key points and stayed on message.

DeepTech companies combine cutting edge science and advanced engineering with the objective of making a profound impact on humanity. One of the sectors most impacted by DeepTech is AI for Drug Discovery. In the second article in our DeepTech Series, we will focus on AI for Drug Discovery and profile four top tier companies in this sector. Drugs are among the most effective ways to combat disease. Drugs are used to fight cancer, relieve pain, prevent cardiovascular disease, alleviate mental illness, cure infectious disease, and much more.

Toronto-based Cyclica President and CEO Naheed Kurji acknowledges that artificial intelligence (AI) is a transformative technology, but he contends it is not the "silver bullet" for drug discovery and development. Instead, he says that AI together with cloud-based computing could serve as a catalyst for a new approach to drug development. Kurji emphasizes that it is important to create "a virtual drug discovery ecosystem where a number of companies who are expert in their space come together and present a more holistic solution than any individual one could do itself because there is no one silver bullet to this problem. The market is so big and there are so many issues, one company can't do it alone." Kurji leads a five-year-old company that has developed and validated a cloud-based platform, called Ligand Express, which uses biophysics, bioinformatics and AI to help pharmaceutical companies navigate the drug discovery pipeline by assessing the safety and efficacy of drugs. The integrated platform enables companies to screen potential small-molecule drugs against repositories of structurally-characterized proteins or'proteomes' to identify significant protein targets. The platform then leverages AI to determine the biological relevance of these targets, and systems biology data to link this information to particular biological pathways or diseases. Kurji says Cyclica's platform, broadly launched in November 2017 already is being used by some of the top 50 pharma companies globally.