- 700 AI BioTech companies and 100 corporations that develop drugs
- 1400 investors in the area of pharmaceutical and healthcare artificial intelligence
- Database with Key Market players in the development of drugs using artificial intelligence
- Comprehensive overview of investments in drug development companies
- In-depth review of notable AI breakthroughs and pharma collaborations in 2021-2022
- Overview of artificial intelligence methods that famous companies use to develop drugs
Report at a Glance
This report, "Artificial Intelligence for Drug Discovery Landscape Overview, Q4 2022", is the latest in a line of studies that DPI has been publishing on the issue of Artificial Intelligence (AI) applications in the pharmaceutical research sector since 2017.
The primary goal of this collection of studies is to give a thorough overview of the industrial environment with regard to the use of AI in drug development, clinical research, and other areas of pharmaceutical R&D. In addition to benchmarking the performance of the major actors who define the space and relationships within the sector, this overview highlights trends and insights in the form of educational mind maps and infographics.
This overview analysis is designerd to assist the reader understand what is going on in the market right now and perhaps provide a glimpse of what is to come.
The report offers technical insights into some of the most recent developments in AI application and research in addition to investment and business trends.
Pharma Efficiency: Challenges
10 years + $2.6 bln = 1 new drug
It takes, on average, over 10 years to bring a new drug to market. As of 2014, according to the Tufts Center for the Study of Drug Development (CSDD), the cost of developing a new prescription drug that gains market approval is approximately $2.6 billion. This is a 145% increase, correcting for inflation, compared to the same report made in 2003.
The solution to this problem comes from three key strategies:
evolution of business models towards more collaboration and early pipeline diversification
implementation of AI as a universal shift towards data-centric drug discovery
discovery of new therapeutic modalities (biologics, therapies, etc.)
0 - Effect on body
I - Safety in humans
II - Effectiveness at treating diseases
III - Larger scale safety and effectiveness
IV - Long term safety
Computer-aided Drug Design
Today's task for the pharma industry is to create a cheap and effective solution for drug development, with companies applying various computational methods to reach that goal. Computer-aided drug design (CADD) is a modern computational technique used in the drug discovery process to identify and develop a potential lead. CADD includes computational chemistry, molecular modelling, molecular design and rational drug design.
Modern computational structure-based drug design has established novel platforms that have a mostly similar structure for testing drug candidates. The usage of AI can simplify and facilitate drug design from filtering datasets for appropriate compounds to advanced lead modification and in silico testing.
Application of AI for Advanced R&D to Address Pharma Efficiency Challenges
Accelerated development of new drugs and targets identification
Identify novel drug candidates
Analyze data from patient samples
Predict pharmacological properties
Simplify protein design
Targeted towards personalized approach
and optimal data handling
Optimize clinical trial study design
Patient-representative computer models
Define best personalized treatment
Analyze medical records
Improve pathology analysis
Time- and resources-efficient information management
Generate insights from thousands
of unrelated data sources
Eliminate blind spots in research
Searching for new applications of existing drugs at a high scale
Rapidly identify new indications
Match existing drugs with rare diseases
Testing 1000+ of compounds in 100+ of cellular disease models in parallel
Optimization of experiments and data processing
Reduce time and cost of planning
Decode open- and closed-access data
Automate selection, manipulation, and analysis of cells
Automate sample analysis with a robotic cloud laboratory
Pharma's “AlphaGo Moment”
Notable Breakthroughs in AI for Pharma
Technological Advancements Defining the Market
Insilico Medicine achieved an industry-first, fully AI-based Preclinical Candidate. The initial hypothesis was built via DNN analysis of omics and clinical datasets of patients. Afterwards, the company used its AI PandaOmics engine for target discovery, analyzing all relevant data, including patents and research publications with NLP algorithms. In the next step, Insilico has applied its generative chemistry module (Chemistry42) in order to design a library of small molecules that bind to the novel intracellular target revealed by PandaOmics. The series of novel small molecules generated by Chemistry42 showed promising target inhibition. One particular hit, ISM001, demonstrated activity with nanomolar (nM) IC50 values.
When optimizing ISM001, Insilico managed to achieve increased solubility, good ADME properties, and no sign of CYP inhibition — with retained nanomolar potency. Interestingly, the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis. The efficacy and good safety of the molecule led to its nomination as a pre-clinical drug candidate in December 2020 for IND-enabling studies. The phase I clinical trial for the novel drug candidate is planned for December 2021.
When optimizing ISM001, Insilico managed to achieve increased solubility, good ADME properties, and no sign of CYP inhibition — with retained nanomolar potency. Interestingly, the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis. The efficacy and good safety of the molecule led to its nomination as a pre-clinical drug candidate in December 2020 for IND-enabling studies.
Comparison of Top-40 Leading AI for Drug Discovery Companies Expertise in Drug Discovery R&D
To learn more about leading companies, check our report.
700 Artificial Intelligence companies: Regional Distribution
1400 Investors: Distribution by Country
50 Leading Investors: Distribution by Country
Big Pharma’s AI-focused partnerships leading to Q4 2022
In this report, we have profiled 700 actively developing AI-driven biotech companies. Steady growth in the AI for Drug Discovery sector can be observed in terms of the substantially increased amount of investment capital pouring into the AI-driven biotech companies ($2.28B in HY 2020 against $126.4B in HY 2022), the increasing number of research partnerships between leading pharma organizations and AI-biotechs, AI-technology vendors, a continuing pipeline of industry developments, research breakthroughs, and proof of concept studies, as well as exploding attention of leading media and consulting companies on the topic of AI in Pharma and healthcare.
Some of the leading pharma executives increasingly see AI as not only a tool for lead identification but also a more general tool to boost biology research, identify new biological targets, and develop novel disease models.
The main focus of AI research today is still on small molecules as a therapeutic modality.
To learn more about collaborations, check our report.
Business activity has been increasing in the pharmaceutical AI space over Q1 2021 - Q4 2022, judging by an increased number of transactions and partnership announcements in this period.
The most significant deals and collaborations include:
Dynamics of Investments in AI in Pharma
There has been a substantial increase in the amount of capital invested in AI-driven pharma companies since 2015. During the last nine years, the annual amount of investments in 700 companies has increased by almost 30 times (to $24.62B in total as of December 2022). The most rapid growth was in 2021, when the year investment in the AI in Drug Development companies was $9.66B. We can suggest that COVID-19 pandemic was the catalyst of this rapid growth. But because of the global economic recession, the investments in AI in Drug Development companies in 2022 are 2.6 times smaller than in 2021 ($3.63B to $9.66B). In December 2022, the total investments in AI in Drug Development companies were $24.62B.
To learn more about the investments in AI in DD, check our report.
Top 10 AI in Pharma Companies by Total Investments
The chart shows the top 10 AI-driven drug discovery companies sorted by the total funding raised by the end of Q4 2022. Charles River Laboratories, an artificial intelligence-powered drug R&D company, is now at the top of the list. The company has the total funding raised to $1.48B. Tempus, a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare, could finance $1.35B in capital market. Relay Therapeutics, Somalogic and Sema4 are new companies due to late-stage mega-rounds during 2022.
Companies related to AI-Pharma
AI in the pharma sector is an integral part of the contemporary pharmaceutical industry. AI-Pharma sector, defined broadly, is not limited to AI companies but also includes pharma, tech, chemistry corporations, and CROs that are engaged in collaborations with AI startups, including but not limited to Mergers & Acquisitions, scientific research, partnerships, and so on. Hence the companies chosen are better described as AI-related or AI-aiming than AI-based solely.
The number of new partnerships between pharma companies and AI companies is ever increasing across the whole industry. On the one hand, AI-focused companies may spend a few years developing all the software and tools which pharma companies do not have. On the other hand, large companies, mainly public ones, have a solid understanding of their science, and extensive experience in the industry and regulatory field, and they are ready to share the risk.
In this chapter, we introduce the list of top corporations related to AI-Pharma that were selected based on the analysis of their R&D, financials, and collaborations with the most promising and advanced AI-Pharma startups.
To learn more about publicly traded companies related to AI-Pharma, check our report.
Notable R&D Use Cases of AI Application in Biopharma
How does MindRank accelerate AI Drug Discovery using AI?
MindRank AI is an artificial intelligence (AI)-empowered drug discovery company. By leveraging its proprietary AI platforms, the company aims to accelerate the drug discovery process and deliver small molecule drugs with desirable potency, efficacy and safety profiles. Molecule Pro is a molecule design and generation platform, Molecule Dance is a molecular dynamics platform to simulate protein movements, and PharmKG is a biomedical knowledge graph to assist with drug discovery.
The company has a team of scientists with extensive experience in small molecule drugs R&D and technological innovation, and their proprietary AI solution has been recognized as one of the top AI breakthroughs in the biopharma industry.
How does Peptilogics empower Drug Discovery using AI?
Peptilogics engineers peptide therapeutic candidates to radically improve the treatment landscape for patients with life-threatening diseases. Uniting biological and pharmaceutical expertise, novel artificial intelligence algorithms, computational biology, and purpose-built supercomputing, Peptilogics is advancing an extensive therapeutic pipeline and accelerating discovery efforts at a pace and scale that was previously impossible.
Peptilogics’ Nautilus™ platform combines generative AI and a suite of predictive models to produce multiparameter-optimized leads for a broad range of targets and therapeutic areas.
Peptides offer advantages over small molecules and biologics. Relative to small molecules, peptides can achieve higher selectivity and safety. Relative to biologics, peptides can more easily reach intracellular targets and cross the blood-brain barrier, and they can be manufactured at reasonable cost to broaden access. With Nautilus™, Peptilogics is enabling peptide generation including high diversity to explore novel chemical space.
Nautilus™ expands on principles demonstrated for the human-designed PLG0206, which is now in Ph1b.
Advantages of Nautilus™
Go beyond just identifying binding hits: encode an expanding list of pharmaceutical properties from the outset
Target-agnostic, therapeutic area-agnostic models that can be applied to both established and novel targets
Access diverse chemical space through proprietary algorithms and in-house, purpose-built supercomputing
Generate effective peptides in specific (tunable) size ranges and complexities, including nonstandard amino acids and cyclic and branched peptides
Interpretable models (where possible)
Validate peptide properties and provide rich data for iterative learning through wet-lab synthesis and assays
Surpass high-throughput screening through biologically informed, multiparameter design of pharmaceutical properties
How does Antiverse engineer the future of Drug Discovery?
Antiverse is a new type of antibody discovery company accelerating drug development. The Antiverse platform exists at the intersection of structural biology, machine learning and medicine to enable breakthroughs to happen more quickly and cost-effectively.
Antiverse prevents diversity loss during amplification to uncover more diverse and rare antibodies.
Antiverse provides more candidates by analyzing NGS data, clustering on multi-dimensional space, and selecting based on sequential and structural grouping.
Existing antibody discovery methods are well-developed and often effective at discovering binders. But when there is a need to find the best possible candidate, or when finding a suitable candidate is hard with current methods, the options are limited and often costly.
Antiverse uses next-generation sequencing (NGS) to extract more data from existing workloads. The AI-Augmented Drug Discovery platform and trained models analyze the statistics gained from thousands of experiments. These outputs are compared against known data in order to select the best candidates.
The Antiverse AI-ADD system found each and every cluster identified by other methods, plus more. These additional clusters contained rare and unique sequences.
How Does Genomenon Use AI in R&D?
Genomenon is an AI-driven genomics company that organizes the world’s genomic knowledge to accelerate the diagnosis and development of treatments for genetic disease.
Genomenon’s Prodigy™️ Genomic Landscapes deliver a profound understanding of the genetic drivers and clinical attributes of any genetic disease, and support the entire drug development process, from discovery to commercialization.
Genomenon’s main focus therapeutic areas are rare diseases, genetic diseases, and hereditary and somatic cancers.
Genomenon’s Prodigy™️ Genomic Landscapes use a unique combination of proprietary Genomic Language Processing (GLP) and expert, scientific review to provide an evidence-based foundation for all stages of the drug development process. These landscapes can be completed at the disease, gene, variant, or patient level, and are maximally comprehensive as a result of GLP. Genomic Landscapes are also rapidly produced using an AI-assisted curation engine that expedites manual review of the data indexed by GLP.
Genomic Language Processing (GLP) is a novel technology that systematically extracts and standardizes genomic and clinical information from the medical and scientific literature. Designed specifically to recognize this complex genomic information, GLP provides superior sensitivity compared to traditional methods, finding more variants and subsequently, more patients. Genomenon’s database, built using GLP, currently contains over 14.8 million variants, 8.8 million full-text articles, and 3 million supplemental datasets.
In collaboration with Alexion, AstraZeneca’s Rare Disease group, Genomenon applied its AI technology to help accelerate the genetic diagnosis for rare disease patients. Genomenon’s novel combination of AI-powered Genomic Language Processing and expert review identified significantly more pathogenic variants associated with Wilson's disease.
Genomenon’s AI-driven approach identified 3.7x more evidence-supported, pathogenic/likely pathogenic variants for ATP7B – a gene associated with Wilson disease – compared to the crowd-sourced database, ClinVar. This significantly expands the resources available to healthcare providers to make more informed diagnostic decisions.
Genomenon’s AI-driven approach identified 3.7x more evidence-supported, pathogenic/likely pathogenic variants for ATP7B than ClinVar.
We predict that this will improve the diagnosis of people living with Wilson disease by improving the ability to interpret genetic testing results.
To learn more about technological insights these and other companies use, check our report.
Selected Pharma AI Industry Developments
To learn more about Pharma AI Industry development, check our report.
Artificial Intelligence for
Landscape Overview Q4 2022
This report offers a thorough analysis of the market environment with regard to the use of AI in drug development, clinical research, and other areas of pharmaceutical R&D. This review compares the performance of the major stakeholders who create the space and relationships within the sector and highlights trends and insights in the form of educative mind maps and infographics.