Monday morning begins: you open Outlook, fix a sentence in Word, skim a new paper, and accidentally drift onto social media where yet another “personal assistant” pops up. The recent surge of generative and natural language processing models has created the sense that artificial intelligence (AI) is capable of addressing every problem we face. Naturally, this assumption extends to drug discovery and development, a scientific endeavour long regarded as a slow, expensive, and uncertain process prone to failure. However, AI’s relationship with drug discovery predates today’s chatbots. Protein-structure and ligand-binding predicting models established the groundwork on which modern deep learning now builds upon. From identifying novel drug targets and designing potential therapeutic molecules to predicting clinical trial outcomes, recent advances in AI offer a new paradigm for pharmaceutical research by accelerating and improving the efficiency of key processes involved in drug development.
Rather than listing AI’s strengths and pitfalls in drug development, the most meaningful way to understand its role in this multi-stage scientific process is to examine its application throughout the drug discovery pipeline. Highlighting its strengths, multiple biotech companies and startups are implementing AI to rethink long-standing workflows from hypothesis generation, candidate development, to commercialization stages.
Personal assistant 1: “Hello! What can I do for you today – identify a target, hit, or lead?”
AI in target identification and drug validation
Despite the rapid progress in understanding thousands of diseases, choosing the correct drug target can be a never-ending task. Human biology is deeply interconnected, meaning that a modification in a single element of a pathway can trigger unintended and undesirable effects. Even when a gene or protein appears promising, researchers must assess whether it can be modulated safely and effectively using reductive experimental setups that have a limited representation of human physiology. AI addresses such challenges in two major ways. Initially, it screens massive amounts of biological data that would otherwise take a long time to process. This allows the identification of relevant pathways and molecules contributing to disease and suggests potential gene-protein-drug relationships of clinical interest with high accuracy and speed. Innovations such as AlphaFold accelerate specialized drug design by providing high fidelity 3D protein structures and potential drug interaction binding models, which helps the development of companies focusing on specific areas. Genialis, for example, analyzes gene data to map tumor biology and elucidate cues that reveal key features of the cancer (biomarkers), as well as predict patient responses. Likewise, the biotech startup Ternary Therapeutics focuses on immunological disorders developing drugs that act like a “molecular glue”. Instead of blocking a protein’s activity, these drugs force two proteins to stick together, triggering the cell’s natural machinery to destroy the target protein associated with the disease.
Once a target is identified, the next challenge is to uncover drug candidates with promising biological or chemical activity: in other words, “hits”. Traditional brute-force and high-throughput screening approaches are increasingly evolving into virtual screening, where millions of compounds are evaluated in silico to predict how well they will attach to the target molecule (binding affinity) and not to unwanted ones (selectivity), whether they will cause harm (toxicity), and how practical they are to manufacture. A leading example is Numerion Labs, formerly Atomwise, founded by University of Toronto (U of T) alumni Dr. Heifets, Dr. Wallach, and Dr. Levy. This U of T Entrepeneurship startup developed AtomNetÒ, the first deep neural network driven molecular screening platform, which transformed the paradigm of hit identification by predicting a compound’s biological effects from its structure. This approach has generated hits for antiviral targets, including Ebola, neurogenerative pathways, and dampening the immune system’s inflammatory signals, several of which have advanced into preclinical development stages. Following a similar approach, Insilico Medicine produced one of the first AI- discovered and -designed small-molecule drug for fibrosis to enter Phase 1. Achieving this milestone in only two and a half years underscores the radical time-to-market optimization facilitated by AI.
Notable AI tools are continuously evolving to decrease the high failure rate of promising drug candidates by re-evaluating hits and guiding iterative designs to maximize therapeutic potential. Canadian companies have become emerging leaders in biologic optimization. Based in Vancouver, AbCellera exploits information naturally encoded in the immune system by using large-scale AI to evaluate antibodies naturally produced against infection at a single cell resolution and selecting those with optimal properties for a particular disease or condition. Their ability to integrate tailored protein-prediction tools of transmembrane proteins, which span the cell membrane and act as communicators with the environment, has been key for their success as these proteins are notoriously difficult to study. Multiple antibodies have already advanced into Phase 1 clinical trials in fields such as endocrinology, inflammation, and soon autoimmunity. Together, these efforts emphasize that AI-driven discovery is not done by a solitary omnipotent entity. Proper guidance of human curiosity and intuition endorses a powerful partnership capable of transforming nature’s molecular repertoire into clinically meaningful therapeutics.
Personal assistant 2: “Great! Do you need any help in transitioning this drug into the market?”
Clinical research and commercialization
AI’s influence is not limited to research and development, it also has the potential to reduce bottlenecks present across preclinical, clinical, and even commercial stages. The predictive modelling behaviour is a major advantage in the clinical landscape. Despite displaying favorable biological activity, most candidate drugs often fail because they lack the necessary properties for moving through the body and being safely cleared. AI simulations help filtering out compounds that are unlikely to succeed in vivo prior to animal testing. Additional models exploring imaging and multimodal data can detect subtle patterns and flag either positive outlooks or safety concerns that often escape human evaluation. Interestingly, AI’s support in clinical trials relies on identifying biomarkers linking treatment response outcomes and refining patient recruitment. Some systems may suggest a combination of treatments based on a patients’ characteristics. While traditional experimentation must not be replaced, AI enables informed decisions that may ultimately increase trial success and identify healthcare gaps that are often breached due to the overwhelming volume of biomedical data. Pharmaceutical companies believe AI could facilitate automated marketing channels in which patients with unmet needs are rapidly identified and drug differentiation strategies developed.
Personal assistant 3: “I am glad I could help. Do you require assistance with anything else?”
Conclusion
Although regulatory agencies like the FDA implement AI to assist in pharmacovigilance, by processing large volumes of reports, no AI-developed drug has received FDA approval. Major criticism relies on the quality of the data in which the models were trained on: “you are what you eat”. Since AI performance will be inherently reliant on the data characteristics, any biases and unrepresentative cohorts, among others, may distort predictions without being acknowledged. More importantly, many deep and machine learning models act as “black boxes”, where accurate outputs are generated without providing the mechanistic reasoning behind the scenes. The inability to trace testable motivations makes regulatory evaluation difficult and limits scientific trust.
In conclusion, AI is reforming every stage of drug development. The discourse of AI as a replacement of human expertise must shift into a collaborative tool, where researchers must nurture tailored computational fluency to guide these models responsibly. Human curiosity alongside computational power will accelerate the delivery of meaningful therapies to patients in a time sensitive manner.
Ana Sofia Mendoza Viruega
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