AI drug discovery is exploding.
Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to create new-age AI solutions that will bring cost efficiency, speed, and precision to the process.
Traditional drug discovery has long been notoriously difficult. It takes at least 10 years and costs $1.3 billion to bring a new drug to the market. And this is only the case for drugs that succeed in clinical trials (only one in ten does).
Hence, the interest in finding new ways we discover and design drugs.
AI has already helped identify promising candidate therapeutics, and it didn’t take years, but months or even days.
In this article, we will explore how AI drug discovery is changing the industry. We will look at success stories, AI benefits, and limitations. Let’s go.
The drug discovery process typically starts with scientists identifying a target in the body, such as a specific protein or hormone, that is involved in the disease. Then they use different methods to find a possible solution, a drug candidate, including:
Once a potential drug candidate (called lead compound) is found, it is tested in cells or animals, before moving on to clinical trials which include three phases, starting with small groups of healthy volunteers, and then proceeding to larger groups of patients suffering from the specific condition.
Artificial Intelligence covers various technologies and approaches that involve using sophisticated computational methods to mimic elements of human intelligence such as visual perception, speech recognition, decision-making, and language understanding.
AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded.
A few AI technologies are empowering drug design.
Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.
ML solutions encompass a diverse array of branches, each with its own unique characteristics and methodologies. These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines. ML has many different application areas, one of which is in the field of AI drug discovery where it enables the following:
Deep Learning (DL) is a subset of ML based on using artificial neural networks (ANNs). ANNs are made up of interconnected nodes, or “neurons,” that are connected by pathways, called “synapses.” Like in the human brain, these neurons work together to process information and make predictions or decisions. The more layers of interconnected neurons a neural network has, the more “deep” it is.
Unlike supervised and semi-supervised learning algorithms that can identify patterns only in structured data, DL models are capable of processing vast volumes of unstructured data and make more advanced predictions with little supervision from humans.
In AI drug discovery, DL is used for:
NLP relies on a combination of techniques from linguistics, mathematics, and computer sciences, including DL models, to analyze, understand, and generate human language. AI drug discovery research often uses NLP to extract information from both structured and unstructured data to accomplish the following:
In the last couple of years, companies across the pharmaceutical sector have taken steps to incorporate AI into their research methods. This includes building in-house AI teams, hiring AI healthcare professionals and data analysts, backing startups with an AI focus, and teaming up with technology firms or research centers.
A combination of factors is driving this trend.
Recent tech advances have shifted the traditional focus of AI drug discovery research.
As the majority of companies in the sector (around 150 in 2022 according to BiopharmaTrend AI Report) continue to be busy with designing small molecules, which are easy to represent computationally and compare at scale, there is also a growing interest in new applications of AI in drug discovery.
Many companies are beginning to embrace AI for designing biologics (77 companies) and discovering biomarkers that indicate the presence or progression of a disease (59). Others are focused on building all-embracing AI drug discovery platforms, identifying new targets, or creating ontologies — structured representations of relationships between different entities such as chemical compounds, proteins, and diseases.
As the shortage of AI talent shows no sign of abating, the entry barriers to AI drug discovery have actually reduced. Tech vendors and pharma giants are releasing increasingly sophisticated AI platforms, including ready-to-use no-code and drag-and-drop systems that enable non-AI experts to integrate artificial intelligence into their research. These developments are playing a major role in the accelerated adoption of AI by the industry.
AI drug discovery projects pursued in academia and the industry have already produced the first successful results across the value chain of drug discovery. Examples include:
AI is a powerful tool that holds the promise of revolutionizing the pharmaceutical industry. With its ability to analyze vast amounts of data and make predictions, artificial intelligence can help researchers overcome the obstacles that have long hindered the drug discovery process by enabling:
According to Insider Intelligence, AI can save the pharmaceutical industry up to 70% of drug discovery costs. The potential of AI in drug discovery is truly exciting, but there are a few roadblocks that need to be tackled first to exploit it to the fullest.
When it comes to AI, it always comes down to input data. Data silos and legacy systems that wouldn’t allow their consolidation are big hurdles to AI research in any domain. In the pharmaceutical industry, the problem may be even more pronounced.
Pharmaceutical companies have traditionally been bad at sharing data, be it results from clinical studies or de-identified patient information, while the troves of data they have may provide answers to questions that the original researcher never considered.
When it ultimately comes to sharing data, it’s often incomplete, inconsistent, or biased, as is the case with datasets used for predicting protein-ligand binding affinities that are crucial for drug discovery. In some cases, the data may not even be reflective of the entire population and the AI model may fall short in real-world scenarios.
The sheer complexity of biological systems makes AI-enabled analysis and predictions of time and spatial changes in their behavior hard.
There is a vast number of complex and dynamic interactions within biological systems where each element such as proteins, genes, and cells can have multiple functions and be affected by multiple factors, including genetic variations, environmental conditions, and disease states.
Interactions between different elements can also be non-linear, meaning that small changes in one element can have a great impact on the entire system. For instance, a single gene that controls cell division can be responsible for the growth of a tumor, or interactions between multiple proteins can lead to the development of highly specific and complex structures such as the cytoskeleton of a cell.
Another challenge is a lack of qualified staff to handle AI drug discovery tools.
The use of neural networks in AI drug discovery has pushed the boundaries of what is possible, but a lack of their interpretability poses a significant challenge. Referred to as black boxes, such AI models might produce the most accurate predictions possible but even engineers can’t explain the reasoning behind them. This is particularly challenging in deep learning, where the complexity of understanding the output of each layer escalates as the number of layers grows.
This lack of transparency can lead to flawed solutions and reduce trust in AI among researchers, medical professionals, and regulatory bodies. To address this challenge, there is a growing need for the development of explainable, trustworthy AI.
New drugs that are changing the game for patients continue to emerge.
Just 15 years after HIV was identified as the cause of AIDS in the 1980s, the pharmaceutical industry has developed a multi-drug therapy that allows people affected by the virus to live a normal life span. Novartis’ Gleevec prolongs the lives of leukemia patients. Incivek from Vertex Pharmaceuticals has doubled hepatitis C cure rates. Keytruda from Merck reduces by 35% the risk of cancer coming back after patients had surgery to excise melanoma.
But not all new drugs are created equal.
A recent analysis of over 200 new medicines conducted in Germany has revealed that only 25% provided significant advantages over existing treatments. The remaining drugs yielded either minimal or no benefits, or their impact was uncertain.
Given the costly and time-consuming nature of drug discovery, it’s clear the pharmaceutical industry needs major changes. And that’s where AI drug discovery could play a role. There is every chance that artificial intelligence can make a transformational contribution going beyond accelerating time-to-clinic.
Thinking about your own AI drug discovery project? Drop us a line. With years of experience in creating AI solutions for healthcare, we are your right partner.