Life Sciences

Transforming pharma: the AI revolution in drug discovery

By By Dr. Emilio Cordova*

The author explores how Artificial Intelligence (AI) is revolutionising drug discovery, enhancing efficiency and paving the way for personalised medicine.

In the realm of drug discovery and development, the integration of artificial intelligence (AI) is not just a trend; it’s a paradigm shift. Traditional methods, characterised by lengthy timelines, high costs and significant failure rates, have long been a source of frustration and unsuccessful attempts at delivering new drug products to market1. However, these challenges are set to change … profoundly.

With the introduction of AI, what we are experiencing is more than just a beacon of hope; its skilful integration with existing drug discovery and development methods and human expertise promises more certainty in less time – with lower costs – benefiting sponsors, patients, and all other stakeholders2. This article delves into how AI is transforming the drug discovery sector by enhancing predictability and efficiency, emphasising the shift toward a more data-driven and efficient approach that leverages both targeted and untargeted models in drug discovery.

AI’s enhanced role in discovery

The journey of drug discovery has always been fraught with challenges. However, the advent of AI is rewriting the narrative. By embedding AI into the drug discovery process, we’re witnessing a significant enhancement in every phase of discovery, from target identification to lead optimisation and more. This integration is not merely about accelerating the process, but about improving it by instilling a level of predictability and efficiency that were previously unattainable using traditional methods.

Data: AI’s discovery catalyst

At the heart of AI’s success in drug discovery is the quality of the data ingested3. AI thrives on data – ideally, lots of it – and its integration into the drug discovery process creates a powerful feedback loop. This loop, enriched by deep experimental expertise – again, more is better – not only refines AI models, but also ensures they evolve in tandem with emerging scientific insights, thereby enhancing their predictive accuracy and operational effectiveness. Consequently, this symbiosis between AI and data transcends traditional discovery methods, paving the way for a new era of innovation where breakthroughs are faster and more aligned with human biology.

Experimental data fuels AI models

The synergy between AI and experimental data is pivotal. The ‘wet science’ data serve as the foundation for training AI models, optimising outputs and improving program success. This symbiotic relationship ensures that AI models are not operating in a vacuum, but are continually refined by real-world experimental results, leading to more accurate predictions and optimised strategies4.

Core of AI modelling

Experimental data act as the lifeblood of AI in drug discovery, encompassing a wide range of information – from biochemical interactions and phenotypic responses to pharmacokinetics and toxicology profiles. These data do more than just feed AI models; they shape their architecture, guiding the learning process to reflect biological realities. By training AI models with diverse and comprehensive datasets, we ensure that the insights and predictions they generate are grounded in the complex nuances of human biology.

AI’s continuous learning cycle

One of the most compelling aspects of AI in drug discovery is its ability to learn and improve over time. Each new experiment contributes data that can refine the model’s predictions, making them increasingly accurate. This iterative process is akin to an ongoing dialogue between the laboratory bench and computational algorithms, where each cycle of feedback sharpens AI’s focus and enhances its predictive power. For instance, data from a failed compound can be as informative about target pathways as a successful one, teaching the AI to navigate the chemical space with greater discernment.

Data-driven success in discovery

The ultimate goal of marrying AI with experimental data is to elevate the success rates of drug discovery programs, a long-standing sore spot in this industry. By leveraging AI’s analytical prowess, researchers can identify the most promising compounds early in the discovery process, prioritise them for development and anticipate potential challenges in safety and efficacy. This approach not only accelerates the pace of discovery, but also allows resources to be allocated more effectively, focusing the attention of sponsors on candidates with the highest likelihood of clinical success.

AI’s practical applications

The integration of AI in drug discovery, particularly in rapidly identifying treatments for emerging diseases, underscores the importance of collaboration and data sharing. Openly sharing data, critical analyses and methods allows AI to aid in various aspects of drug discovery, including in silico property prediction and the identification of effective drug candidates. AI algorithms can efficiently scan large compound libraries for potential candidates, thus showcasing their ability to guide experimental screening efforts with limited initial data.

The future of AI-designed drugs

The future of drug discovery is being reshaped by the convergence of AI and experimental data, a partnership that promises to revolutionise how we design molecules and approach therapeutic challenges5. As we look ahead, AI’s role in drug discovery is poised to transition from an auxiliary tool to a central figure in the design, testing and optimisation of new compounds. This shift is predicated on AI’s ability to process vast datasets, drawing insights that would otherwise be unattainable to human researchers alone. The integration of AI with cutting-edge experimental techniques will enable the design of molecules with unprecedented specificity and efficacy, reducing the time and cost associated with bringing new drugs to market.

Toward personalised medicine

The evolution of AI models, fuelled by richer and more comprehensive datasets, will lead to more accurate predictions of drug behaviour and interactions within the human body. This advancement holds the key to personalised medicine, where treatments can be tailored to the genetic makeup of individual patients6. AI’s ability to sift through genetic information and correlate it with drug responses will open new avenues for customised therapies, making treatments more effective and reducing the incidence of adverse reactions.

Exploring complex disease pathways

As AI technologies mature, their application will extend beyond traditional drug design to explore complex biological systems and disease pathways. AI models will be instrumental in uncovering novel targets and understanding the multi-faceted nature of diseases like oncology, CNS, cardiovascular and autoimmune disorders, to name a few. By simulating the interactions within these systems, AI will identify new therapeutic opportunities and guide the development of drugs that can modulate disease processes more effectively.

AI and lab data synergy

The future will also see a deeper integration of AI with experimental validation, creating a seamless feedback loop that accelerates the discovery process. This synergy will ensure that AI-designed molecules are not only theoretically effective, but also validated through empirical data. The continuous exchange of data between AI models and laboratory results will refine AI predictions, making the drug discovery process more efficient and reliable.

In addition, this integration will facilitate the development of more sophisticated AI algorithms, thereby enhancing the predictive accuracy for drug efficacy and safety profiles before clinical trials commence. Furthermore, the democratisation of drug discovery through AI enables all levels of research to leverage cutting-edge research tools, broadening the scope of innovation and potentially reducing the time and cost to market for new treatments.

The future in a nutshell

The horizon for AI in drug discovery is expansive, promising to streamline the discovery of new therapies and usher in an era of personalised, effective and accessible treatments. As AI and experimental science fuse, the drug discovery landscape will be transformed, with a focus on leading the charge toward this exciting future. This shift is expected to accelerate innovation, diversify the pipeline of drug candidates and potentially reduce the overall cost of bringing new treatments to market, marking a significant step forward in making advanced drug discovery tools accessible to a broader range of researchers and companies.

Embracing drug discovery

As we stand on the brink of a new era in drug discovery, the integration of AI with experimental data heralds a transformative shift in how we approach the creation of new medicines. This revolution, characterised by a seamless blend of computational power and biological insight, is not merely about enhancing the efficiency of drug discovery processes; it’s about fundamentally redefining what is possible in the quest to treat and cure diseases. The burgeoning field of digital biology, powered by AI, is poised to turn biology into an engineering discipline, opening unprecedented opportunities for drug discovery7. Furthermore, the rapid advancement of AI-designed drugs through both the discovery and preclinical stages, as demonstrated by recent clinical trials, underscores AI’s potential to significantly accelerate the drug development process. This acceleration is not confined to a single entity; rather, it is a testament to the collaborative efforts across the scientific community, driving forward with innovations that promise to reshape the landscape of pharmaceutical research and development.

The journey of AI in drug discovery, from its nascent integration to becoming an indispensable tool, illustrates a path filled with challenges, learning and, ultimately, immense rewards. The ability of AI to sift through and make sense of vast datasets, to predict outcomes with increasing accuracy and to uncover insights that elude human cognition, is a testament to the power of this technology. Yet, it is the marriage of AI with the rich, nuanced data from experimental research that truly unlocks its potential. This partnership does not replace the human element, but rather enhances it, allowing scientists to explore new hypotheses, to test the boundaries of current knowledge, and to accelerate the pace at which new treatments can be developed and brought to market.

Looking to the future, the promise of AI in drug discovery is boundless. With each advance in AI technology and each new dataset generated from experimental research, we edge closer to a world where drug discovery is more predictive, personalised and potent. The collaborative efforts of researchers, leveraging AI and experimental science, are crucial as we navigate this uncharted territory, blending the art and practice of science with the precision of advanced algorithms to forge new paths in healthcare. As we embrace the AI revolution, we do so with the knowledge that we are not just witnessing a change in how we discover drugs, but are participating in a historic moment that will define the future of medicine.

References

  1. Fleming, N. How artificial intelligence is changing drug discovery. Nature. 2018;S55.
  2. Ayers, M., et al. Adopting AI in Drug Discovery. Boston Consulting Group. March 29, 2022. Accessed April 8, 2024.
  3. Blanco-Gonzalez, A., et al. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. 2023;16(6):891.
  4. Barzilay, R., et al. The race for a cure. Royal Society of Chemistry. June 3, 2020. Accessed April 8, 2024.

5 Jiménez-Luna, J., et al. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery. 2021;16(9):949-959.

  1. Khatami, S.G., et al. Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures. npj Systems Biology and Applications. 2021;7(1):40.
  2. Regalado, A. An AI-driven ‘factory of drugs’ claims to have hit a big milestone. MIT Technology Review. March 20, 2024. Accessed April 8, 2024.

______________________________________________________________________________*Dr. Cordova has served as executive director of Logica® since June, 2023. Prior to his appointment at Logica, he was chief executive officer of SAMDI Tech Inc for eight years, culminating in the successful acquisition by Charles River in 2023. He brings over 20 years of management and executive experience in contract research. Throughout his career, he has held leadership positions in sales, marketing and business development, including positions at Worldwide Clinical Trials, Bioanalytical Systems Inc, which merged with Seventh Wave in 2018 to form Inotiv, and AIT Bioscience (now Q2). He has a PhD from the University of Miami and an MBA from Purdue University’s Krannert School of Management. In addition, he completed an NIH post-doctoral fellow appointment at Harvard University under the guidance of Dr. George Whitesides.