Artificial Intelligence in Drug Discovery

Artificial intelligence (AI) is currently drawing increased attention in many aspects of science and technology. AI is the simulation of human intelligence processed by machines, especially computer systems using algorithms. Recently, application of AI demonstrated potential to revolutionize highly expensive and time-consuming drug discovery process. AI provides improved efficiency, accuracy and speed to the drug discovery process.

AI is a set of different methodologies and technologies that use advanced computational approaches to emulate human decision-making. Among them, the machine learning (ML) and deep learning (DL) are noteworthy for drug discovery. However, DL that engages artificial neural networks (ANNs) is the predominant approach. The AI techniques used in the drug discovery can be classified into two main categories, namely supervised learning and unsupervised learning. The unsupervised learning methods are often used for exploratory data analysis to find the hidden data patterns, whereas, the supervised learning is broadly based on a set of input data to train the algorithm and correctly predict or classify the outcomes within similar data set. The integrated AI tools are modernizing nearly every stage of the drug discovery process, such as, target identification, molecular simulations, prediction of drug properties, de novo drug design, synthetic route generation and drug-candidate prioritization. The illustrative application of AI techniques to drug discovery and evaluation is summarized in Figure 1 [1].

Figure-1: Outline of AI technique application to drug discovery and evaluation

Advantages of AI for drug discovery [2]

The traditional drug-target identification and hit generation for drug discovery mostly rely on the virtual screening of millions of compounds – slow and expensive process. AI can quickly generate ultra-large virtual libraries exploiting the large datasets available in journals, patents, books, dissertate, reports, conference, clinical trials, etc. AI-based algorithms are used to identify the specific proteins or genetic pathways involved in diseases, druggable binding sites in proteins and binding affinity of molecules. The efficacy and toxicity of drug candidates are of utmost significant since many candidates face challenges in progressing through clinical trials due to the issues related to efficacy and toxicity, despite significant investments of effort and resources. AI is playing a crucial role in predicting efficacy and toxicity efficiently. On the other hand, properties such as ADME, PK and PD profiles, solubility, bioavailability, plasma protein binding of a drug candidate are crucial to enter into clinical trial. AI can play a significant role to predict the properties of a drug candidate by analysing data on functionality, structural features, hydrophobicity and the size of molecules. For the clinical trials, AI can help patient selection and recruitment by processing large amounts of electronic health record (HER) data as well as monitoring patients during the trial. Finally, AI approaches can be employed to gather empirical evidence via mining EHRs for pertinent information.

Limitations of applying AI in drug discovery

In spite of the potential advantages of AI in drug discovery, there are several challenges and limitations. AI relies heavily on the availability of a substantial amount of data. The limited availability and reliability of data can often affect the accuracy and reliability of the results. It is important to note that AI-based approaches should not be considered as a replacement for the conventional experimental methods developed by highly skilled and experienced human researchers. AI can only provide predictions based on the data available, but the findings should subsequently be validated and interpreted by human investigators.

Some of the fundamental challenges faced by AI approaches include generative capacity to bring forth novel ideas that have limited presence in the datasets. Moreover, at the current state of development AI tools are incapable of sophisticated judgement mechanisms used to modify various weighing criteria. Therefore by and large AI is limited to incremental innovations, which nonetheless may have significant ramifications.

Success using AI in drug discovery

Many biotech and pharmaceutical companies are now allocating substantial financial resources towards AI-driven drug discovery. Currently, numerous AI-designed drugs are undergoing clinical trials, which are summarized in Table 1 [3].

Table 1: Selected AI-designed drugs in or entering clinical trials

Exscientia, a UK-based AI-driven drug discovery company, has successfully identified three drug candidates: EXS-21546, GTAEXS617, and EXS4318 in collaboration with Evotec (Germany), Apeiron Therapeutics (China), and Bristol Myers Squibb (USA). These candidates are highly selective inhibitors of A2A receptor, protein kinase C-theta (PKC-θ), and CDK7, respectively. Currently, they are undergoing phase 1/2 clinical trials for the treatment of cancer and inflammatory diseases. Presently, Bristol Myers Squibb has 16 AI-designed drug candidates in its pipeline, including drugs for COVID-19, tuberculosis, malaria, and hypophosphatasia. Another prominent clinical-stage TechBio company, Recursion (USA), boasts a robust portfolio of AI-assisted drug candidates. Notably, REC-2282, REC-994, REC-4881, and REC-3964 are currently undergoing phase 1/2 clinical trials for the treatment of neurofibromatosis type 2, cerebral cavernous malformation, familial adenomatous polyposis, clostridioides difficile Infection, and APC mutant cancers. INS018_055, a small molecule inhibitor for the treatment of idiopathic pulmonary fibrosis (IPF), was developed by a Hong Kong-based company, Insilico Medicine. Unlike other AI-aided drugs currently in trials, INS018_055 is the first drug candidate fully generated by artificial intelligence that has entered clinical trials with human patients. BenevolentAI (UK) is currently conducting phase 1/2 clinical trials for two drug candidates, BEN-2293 and BEN-8744. These candidates are being developed for the treatment of Atopic dermatitis and Ulcerative colitis, respectively. The RLY-4008, a highly selective inhibitor of FGFR2, developed by Relay Therapeutics (USA) is under phase1 clinical trial for the treatment of FGFR2-altered cholangiocarcinoma.

As the importance of AI in drug discovery continues to increase, a wide range of approaches and diverse AI tools are being employed across various domains, including clinical trial design, manufacturing, and more. Drug discovery faces significant challenges; however, the combination of human expertise and intelligence, along with AI, holds the potential to offer improved prospects.

References

[1] Wei Chen, Xuesong Liu, Sanyin Zhang and Shilin Chen; Mol. Ther. Nucleic Acids 2023, 31, 691–702
[2] Alexandre Blanco-González, Alfonso Cabezón, et al; Pharmaceuticals 2023, 16, 891
[3] Carrie Arnold; Nat. Med. 2023, 29, 1292–1295  (also for top figure)