Clinical Research Insider Summit No. 11

Computational technologies for drug discovery

Drug development is a complex process because there are still many unknowns in human diseases. For decades and particularly in recent years, computational models and, in particular, artificial intelligence (AI) models have been integrated into basic research and decision-making during the drug design process in combination with experience-based human intelligence and intuition. This manuscript discusses recent examples of AI applications in drug design and development. The concepts and computational approaches used are discussed, and the possibilities and limitations of drug design using AI are analyzed.

Artificial intelligence is being incorporated into our daily lives. For example, voice assistants and image recognition work with AI. The algorithms process and transform images, words, or phrases from a natural language into a format readable by computer programs. During this process, algorithms identify rules to relate data and extract patterns and information to make predictions that make it easier to make decisions and/or solve problems. Scientific publications that include AI terms such as “machine learning” (ML), referring to machine learning or machine learning , or “deep learning” (DL), referring to deep learning, are increasingly frequent.

Since the 50’s, AI has been used in various areas of Chemistry. However, technological advances and the parallel development of theories and models have accelerated its application. Adopting AI in the drug development process faces challenges such as low success rates, where more and more money is invested in obtaining new drugs and only few molecules reach clinical phases. In general, the average time required for discovery, preclinical and clinical studies, approval and marketing of a drug is approximately sixteen years and costs a median of 985 million dollars. Implementing AI can reduce the development time of a new drug to just two or three years.

Recently, the term “augmented intelligence” was integrated into the model of association between human intelligence and AI with the aim of improving cognitive performance, learning, decision making and the generation of new experiences. Under the concept of augmented intelligence, it is not intended that machines replace scientists, but that scientists understand AI methodologies, allowing them to move into the field of systems biology and take advantage of their own experience, along with the capabilities offered by AI models.

The goal of QSAR (quantitative structure-activity relationships) is to find a mathematical model that approximates the intrinsic associations between chemical structures and biological activity. Currently, AI algorithms are seeing numerous applications within the different stages of the drug discovery and development process. For example, in the early stages, AI methods have had a substantial impact on predicting the threedimensional (3D) structure of proteins. They have also helped suggest a large number of drug-like compounds to effectively test their biological/ biochemical activity in high-throughput screening (HTS) studies.

The role of AI in the prediction of properties related to absorption, distribution, metabolism, elimination and toxicity (ADME-Tox) is an important part of decision making, as it facilitates the selection of compounds for synthesis and biological evaluation. In this area, AI models are used to plan and optimize compound syntheses, to estimate the synthetic accessibility of compounds present in chemical libraries that may have thousands or millions of compounds, and to automate the synthesis process. AI methods are also employed at preclinical stages to identify tissue-specific biomarkers and at clinical stages for patient selection in clinical trials, to identify new uses of existing medicines, and in pharmacovigilance, to understand and prevent adverse drug effects.

Methods and applications of AI in drug design

The science of the XXI century is having great transformations due to the increasing access of information available in databases, many of them public. To make sense of the information contained in the data requires new ways of thinking and learning and new work methodologies. Examples of the use of AI methods in drug research are discussed below.

3D structure prediction of therapeutic molecular targets

The design of drug candidates based on the 3D structure of the therapeutic target has been a successful strategy. The hypothesis of structurebased design is that compounds with affinity for functional sites of these structures can induce or result in a clinical effect. Notable examples of this strategy are the development of antivirals such as relenza and drugs used to treat AIDS. X-ray crystallography, nuclear magnetic resonance and electron microscopy make key contributions to elucidating the 3D structure of molecular targets. 3D structures can also be employed in computer-aided design, employing molecular coupling, fragment-based design, and de novo design. Although the 3D coordinates of almost 200,000 structures are available in the Protein Data Bank to date, it is laborious and expensive to experimentally elucidate the structures of all therapeutic targets. Here, AI has advances to predict the folding of proteins from the knowledge of their sequence.

De novodesign of new compounds

Generative models, de novo or “from scratch”, are used to design new chemical compounds. Examples of DL architectures used in de novo design are: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Recurrent Neural Network (RNN). In addition, hybrid approaches can be designed. Generative design has made considerable progress, however, more evidence of its efficiency is required. Current challenges of generative approaches include the chemical synthesis of compounds and the various solutions for a given property.

Systematic evaluation of molecular targets

Understanding the mechanism by which a compound exerts therapeutic effect is essential for drug design and optimization. Identifying biological targets for bioactive compounds is considered a bottleneck to the informed design of “chemical probes” and lead drugs. Fortunately, high-throughput experimentation has generated a wealth of chemical and biological data that makes it possible to produce models to link biological targets with bioactive compounds. These data have shown that compounds are rarely selective for a single given biological target. The interaction of a compound involving several related or unrelated macromolecules is known as polypharmacology that is exploited in the design of multi-target drugs and in drug repositioning.

Prediction of ADME-Tox properties

The number of organic molecules that could potentially be synthesized in a drug discovery program is approximately 1063. To limit the number of molecules to be considered in the real world, there are AI applications to predict the ADME-Tox properties of a molecule before its synthesis. Properties such as aqueous solubility, intestinal permeability and plasma protein binding are important parameters to consider in achieving systemic exposure of an oral drug. Similarly, knowing how quickly a drug is metabolized and eliminated, and what the mechanism of action is, determines the success of a drug, since these properties have an impact on the bioavailability and safety of a drug. Toxicity is also a critical parameter to evaluate not only in the pharmaceutical industry but also in cosmetics and agrochemicals. The number of possible causes for a compound to fail and have adverse effects is large and, as such, the number of properties to check in a laboratory is expensive and time-consuming. In this context, AI models help improve predictions of drug efficacy and safety.

Chemical synthesis

AI techniques are also used to analyze chemical synthesis information available in the literature and databases. For example, different reactions can be compared and processed to produce a given compound. This information can lead to: a) retrosynthetic analyses that divide the problem into subproblems, and generate stepby-step synthesis suggestions, b) in the recommendation of reaction conditions that will lead to a successful direct reaction and c) in the prediction of products from a set of materials and initial conditions that is used to validate the proposed synthetic steps. It is worth mentioning that, for a synthetic planning tool to help increase chemical knowledge and accelerate the synthesis process, it must be easy to use and be intuitive in its handling and interpretation of results. In addition, the suggested routes should result in materials that can be purchased in the market.

Personalized medicine

In classical drug design and development, it is assumed that the clinical response of patients is approximately the same. However, the response to a clinical treatment may vary from patient to patient. All of these factors are difficult to consider systematically from the outset in a drug design program and therefore further hinder the successful therapeutic use of drugs. In this context, the adoption of AI and other innovative technologies, and the use of the great information available in multiple sources, allows specific, more precise treatments and changing the health system towards a future where medicine is personalized, predictive, preventive and participatory. In the future, the inclusion of more diverse data including understudied populations, the implementation of strategies to minimize exclusion and bias, and new AI technologies are expected to have a significant impact on treatments and patient outcomes.

Drug discovery and development combine computational, experimental and clinical methods. There is currently no single solution that guarantees successful drug discovery and it is important to remain receptive to new technologies. The socalled “augmented intelligence” has applications in the process of drug discovery and development, in decision-making, and also in the development of more effective therapies from already approved drugs. The variability in the response among patients to the same therapeutic treatment imposes another challenge.

Additional challenges include lack of data transparency and collaboration; the lack of interdisciplinary trained personnel and efficient human communication between research groups. However, AI methods provide the opportunity to diversify applications in drug design, provide better predictive models, streamline research and help propose effective treatments in a personalized way. Proper validation of AI results will allow feedback and improvement of existing models and lay the groundwork for new paradigms.

The proper integration of basic and more sophisticated bioinformatics, computing, nanotechnology and pharmacogenomics strategies is expected to lead to the next stage of advances in successful drug discovery and therapeutic use.

Acknowledgements

To the program of Research Projects in Artificial Intelligence in the Innovation Space UNAM –HUAWEI, project no. 7, and to the DGAPA, UNAM, Program of Support for Research and Technological Innovation Projects (PAPIIT), project No. IN201321. F.I. S-G. thanks CONACyT, Mexico, for PhD Scholarship No. 848061.

Jose L. Medina Franco

Contact: medinajl@unam.mx

Fernanda I. Saldivar Gonzalez

DIFACQUIM research group, Department of Pharmacy, Faculty of Chemistry, National Autonomous University of Mexico