AI in Drug Discovery

The application of artificial intelligence (AI) has been increasing magic in various sectors of the society. Particularly the pharmaceutical industry, we have seen the impact of it during pandemic period. The use of AI in diverse sectors of the pharmaceutical industry is phenomenal, including drug discovery, development, drug repurposing, improving pharmaceutical productivity, and clinical trials. AI reduces the human workload as well as achieving targets in a short period of time.

AI: networks and tools

AI involves several method domains, such as reasoning, knowledge representation, solution search, and, among them, a fundamental paradigm of machine learning (ML). ML uses algorithms that can recognize patterns within a set of data that has been further classified. A subfield of the ML is deep learning (DL), which engages artificial neural networks (ANNs). These comprise a set of interconnected sophisticated computing elements involving ‘perceptions’ analogous to human biological neurons, mimicking the transmission of electrical impulses in the human brain. ANNs constitute a set of nodes, each receiving a separate input, ultimately converting them to output, either singly or multi-linked using algorithms to solve problems. ANNs involve various types, including multilayer perceptron (MLP) networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), which utilize either supervised or unsupervised training procedures.

The MLP network has applications including pattern recognition, optimization aids, process identification, and controls, are usually trained by supervised training procedures operating in a single direction only, and can be used as universal pattern classifiers. RNNs are networks with a closed-loop, having the capability to memorize and store information, such as Boltzmann constants and Hopfield networks. CNNs are a series of dynamic systems with local connections, characterized by its topology, and have use in image and video processing, biological system modeling, processing complex brain functions, pattern recognition, and sophisticated signal processing.

Several tools have been developed based on the networks that form the core architecture of AI systems. One such tool developed using AI technology is the International Business Machine (IBM) Watson supercomputer (IBM, New York, USA). It was designed to assist in the analysis of a patient’s medical information and its correlation with a vast database, resulting in suggesting treatment strategies for cancer. This system can also be used for the rapid detection of diseases. This was demonstrated by its ability to detect breast cancer in only 60s.

AI in the lifecycle of pharmaceutical products

Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predict key drivers in sales of pharmaceuticals, thus helping marketing executives to allocate resources for maximum market share gain, reversing poor sales and enabled them to anticipate where to make investments.

AI in drug discovery

The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

The virtual chemical space is enormous and suggests a geographical map of molecules by illustrating the distributions of molecules and their properties. The idea behind the illustration of chemical space is to collect positional information about molecules within the space to search for bioactive compounds and, thus, virtual screening (VS) helps to select appropriate molecules for further testing. Several chemical spaces are open access, including PubChem, ChemBank, DrugBank, and ChemDB.

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilized to revolutionize the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmers because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.

Future is more brighter

The biggest opportunity for the future, according to Friedrich Rippmann, is the acceleration of drug discovery and reduction of attrition rates, ultimately making more novel drugs available to patients, faster.

“There are so many opportunities to apply AI in drug discovery,” he says. “But at the moment we’re hindered at times by the prohibitive costs involved. As more competition emerges though, we will see costs coming down – opening up exciting possibilities for new discoveries in diverse fields.”

We want to input the parameters our molecules should satisfy, and be able to efficiently generate novel molecules to meet those parameters,” explained Cohen.

This approach, called multiparameter optimization, could improve a drug’s target selectivity, and lower toxicity by minimizing unwanted interactions with off-targets.

Furthermore, multiparameter optimization can lower the number of compounds synthesized to find potential drug candidates. It can also decrease the number of design cycles a drug candidate goes through in order to be validated.

AI is unlikely to completely replace human ingenuity in drug discovery any time soon. Closer collaboration, trust, and crosstalk between AI engineers and life scientists are essential to realizing its full potential in drug discovery.