Pharmaceutical AI in 2021: Key Developments So Far

Introduction

According to GlobalData’s report ‘Smart Pharma’, 28 percent of all the surveyed companies plan on using artificial intelligence (AI) and big data technologies to optimize drug discovery and development processes in the next two years. Furthermore, 32 percent of respondents would be employing big data analytics and predictive technologies to streamline sales and marketing efforts. This indicates growing practical importance of AI in both scientific and operational aspects of the pharmaceutical business.

Artificial intelligence in a nutshell is a field of science concerned with creating intelligence agents -- systems capable of taking in data from the surrounding world, processing it in some intelligent way, and outputting meaningful results. A powerful sub-field of artificial intelligence is machine learning (ML) -- a broad scope of algorithms capable of improving its prediction accuracy with each new iteration, or with more data available for training. Both artificial intelligence and machine learning (ML) are old concepts, known for decades, but the actual AI revolution in many industries and in pharma, in particular, has been enabled by a specific type of machine learning -- deep learning (DL). This family of AI models is based on the architecture of neural networks, and owing to the unprecedented ability to learn sophisticated data representations -- especially from multimodal data -- deep learning algorithms can provide insights into data unattainable by any other statistical methods and models. It is deep learning that drives all the buzz around the area of artificial intelligence (sometimes very well deserved, other times -- overhyped), and it is deep learning that is set to disrupt the way drug discovery research is done. 

According to BiopharmaTrend’s interactive report “The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D”, various machine learning and deep learning models can be applied to improve a wide range of research tasks in drug discovery, including de novo drug design, structure-activity relationship (SAR) prediction; improvement in 3D protein structure simulation; biomarker discovery, patient stratification, predicting drug responses; analysis of high-content screening results (e.g. cell images) in real-time, correlation to other types of data; improving drug repurposing programs, streamlining clinical trial operations, and much more. 

AI in drug discovery yields breakthroughs

While there is an abundance of proof-of-concept studies in applying artificial intelligence in drug discovery, several success stories are especially illustrative. 

One such case is Insilico Medicine, which recently reported a new drug candidate for kidney fibrosis, having repeated their previous success in identifying a drug candidate for idiopathic Pulmonary Fibrosis (IPF) -- all using their drug design system Chemistry42, based on generative adversarial neural networks (GANs). Both their successes were enabled by a more important discovery -- that of a novel pan-fibrotic target, revealed by their target discovery platform PandaOmics. The entire process from the initial target hypothesis to the preclinical drug candidate nomination took under 18 months and a fraction of the cost of “traditional drug discovery”. 

Another prominent example is DeepMinds AlphaFold2 -- the AI system for predicting the 3D shape of proteins based on their primary amino acid sequence. It is believed to have changed the paradigm of modern structural biology -- just recently, the company released predicted structures of as much as 350.000 proteins, including nearly every protein expressed in the human body. 

The AlphaFold Protein Structure Database of the human proteome was published in Nature and is made openly available for the drug discovery community. 

Next, in April 2021, Evotec announced a phase 1 clinical trial on a new anticancer molecule, created in partnership with Exscientia -- a UK-based drug discovery company, specializing in AI-driven small molecule design. It took Exscientia around 8 months to come up with an A2 receptor antagonist efficient in helping T cells fight solid tumors -- using their AI platform Centaur Chemist. In 2020, Exscientia in partnership with Sumitomo Dainippon Pharma developed a selective serotonin reuptake inhibitor (SSRI) designed to treat obsessive-compulsive disorder (OCD), which has also advanced to a clinical state. 

The growing ecosystem of organizations developing or adopting pharmaceutical artificial intelligence is represented by more than 280 drug discovery companies in the relatively early stage of their development (most founded within the last 5 years). Several of them managed to raise hundreds of millions in venture capital or even went on IPOs just recently. 

Besides, almost all leading pharmaceutical organizations (‘big pharma’) and largest contract research organizations (CROs) are intensively developing (or at least exploring) in-house AI-driven systems and data-centric workflows in R&D and operations, and actively partnering with AI-vendors and R&D platform providers. A searchable database of such collaborations and financial rounds is available in our report “The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D”, however, below we will list some notable examples to illustrate the industry developments. 

One important trend now is the expansion of leading non-pharmaceutical tech corporations, such as Google, Microsoft, Tencent, Baidu, Nvidia, Intel, etc, into the pharmaceutical research and biotech industry -- something that is largely enabled by their cutting-edge technologies in AI and big data. Besides, a wide array of technology providers of a smaller scale are increasingly present in the pharmaceutical market -- from big data companies, like 23andMe, to quantum computer developers, like Zapata Computing, and autonomous lab developers, like Arctoris and Strateos. 

A growing ecosystem of advanced technology providers operating in the pharmaceutical industry and biotech is profiled in a report by Deep Pharma Intelligence “Landscape of Advanced Technology Companies in Pharmaceutical Industry Q2 2021”. 

Now, let’s review some of the notable industry developments related to the use of various artificial intelligence technologies, such as deep neural networks and natural language processing (NLP) tools being applied in drug discovery and clinical research. The focus here is on major investment rounds, illustrative R&D partnerships between AI-driven companies and “traditional” pharmaceutical organizations. We will also review new AI-driven biotech startups founded in 2021. This is not meant to be a comprehensive overview, rather – an illustrative list of developments and reflections on where things are moving forward. For comprehensive coverage of the pharmaceutical AI ecosystem, check our interactive report “The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D”.