Micro/Small Cap Companies Using AI In Drug Discovery Up 35% In November -

Micro/Small Cap Companies Using AI In Drug Discovery Up 35% In November

Dec 06, 2023Jeremy ParkinsonFinance0

Photo by Mohamed Nohassi on Unsplash
The pharma industry is embracing artificial intelligence (AI) to streamline drug discovery and development and is rapidly expanding. Back in 2019, it was reported that 132 startups were using AI in drug discovery and today that number has grown to 491 according to status-insights.com (see here). The global “AI in drug discovery” market was worth about $1.1 billion last year but should grow at a 30% clip from 2023 to 2030, according to Grand View Research. This article highlights 10 micro- and small-cap companies exploring the use of AI in drug discovery and drug development. Source

Making a new drug is like finding a needle in a haystack
Making a new drug is like finding a needle in haystack – and AI can find that needle in that haystack very, very quickly – possibly measured in days rather than years:

AI can map out human genetic data in an instant,

AI can identify mutations in that data in an instant,

AI can run simulations of compounds to fight those mutations in an instant.

That effort is well underway:

Research firm Deep Pharma Intelligence estimates that investments in the field of AI-powered drug discovery have tripled over the past four years to nearly $25 billion.

Morgan Stanley believes that AI-powered drug discovery will lead to an additional 50 novel therapies being brought to market over the next decade, with annual sales in excess of $50 billion! In other words, a $50 billion AI drug discovery revolution is underway.

Source: Deep Pharma Intelligence

The Clinical Trial Process
New drugs are currently approved through human clinical trials: rigorous, year-long procedures starting in animal trials and gradually moving to patients in trials who are exposed to side effects that cannot be predicted or expected. The process typically cost billions of dollars and take many years to complete, sometimes more than a decade, and, even if their trials are successful, they still have to receive approval of a country’s respective regulatory agency. Source Source: Biosourcing

Why Use AI?

In its simplest form, AI is just a number of machine learning algorithms scouring millions of data to parse and analyze every possible connection in that data so as to draw meaningful conclusions from – and make predictions based on – that data.

Luke Lango of incestorplace.com summarizes the above by saying “That’s really all AI is – it is just algorithms and data, with data acting as the “fuel” of the model and, much like the amount of fuel in the tank determines how far a car can drive, the quantity and quality of data an AI model has access to ultimately determines the quality of the AI model itself. The more data, the better the AI. It’s that simple.

When it comes to the human body, there is no dearth of quality data. Humans – like computers – are really nothing more than a bunch of data strung together – a bunch of As, Gs, Cs, and Ts strung together – or the four base types found in human DNA molecules – with each determining a person’s characteristics, traits, and even actions and when AI is applied to all that data it will have a huge impact on the medical industry.” Source

Source: NCRAD

Artificial intelligence technology, however, helps companies aggregate and synthesize a lot of information that’s needed for clinical trials, thus shortening the drug development process. It can also help understand the mechanisms of the disease, establish biomarkers, generate data, models, or novel drug candidates, design or redesign drugs, run preclinical experiments, design and run clinical trials, and even analyze the real-world experience. Source

A study by Janssen Research & Development (JNJ arm) concludes that the AI method to be up to 250 times more efficient than the traditional method of drug discovery. It holds the potential to reduce timelines for drug discovery, to increase accuracy of predictions on efficacy and safety as well as to create better, and more, opportunities to diversify drug pipelines.

The following 10 AI in drug discovery and drug development clinical-stage companies (see here) trade on various Canadian and American stock exchanges. The list is divided into the top 5 by market capitalization, the next 5 with their stock performances in the past month of November and YTD, a description of each, their November month-end stock prices, and links to the most recent news on each where present.

Schrödinger (SDGR): UP 43.6%; UP 66.3% YTD

Company Description:
- SDGR offers specialized solutions for both small molecule discovery and biologics discovery focusing on structure prediction and protein engineering, including antibody modeling.
Market Capitalization: $2.3B
Month-end Stock Price: $31.09
News: Schrödinger Reports Third Quarter 2023 Financial Results

Recursion Pharmaceuticals (RXRX): UP 29.7%; DOWN 11.2% YTD

Company Description:
- RXRX specializes in drug discovery through machine learning using its proprietary Recursion Operating System and has one of the world’s most extensive biological and chemical datasets.
- It has several compounds in phase 1 and 2 studies, including a small molecule therapeutic for cavernous cerebral malformation and another for neurofibromatosis type 2.
- Recursion claims to conduct millions of experiments per week using supercomputers, machine learning and automated robotic labs.
Market Capitalization: $1.5B
Month-end Stock Price: $6.85
News: RXRX Could Soar by 120%, According to Wall Street

AbCellera Biologics (ABCL): UP 20.5%; DOWN 53.5% YTD

Company Description:
- ABCL develops antibody therapeutics using AI focusing on searching and analyzing the immune systems to find potential antibodies, then outsourcing their initial findings to their partners for further drug discovery.
Market Capitalization: $1.3B
Month-end Stock Price: $4.71
News: AbCellera Reports Q3 2023 Business Results

Relay Therapeutics (RLAY): UP 30.5%; DOWN 33.8% YTD

Company Description:
- RLAY specializes in developing an artificial intelligence-driven allosteric drug-discovery platform intended to detects and characterizes interactions that occur on a protein of interest and combines computational methods with experimental approaches across the fields of structural biology, biophysics, and chemistry. Its initial focus on precision oncology and genetic diseases.
Market Capitalization: $1.1B
Month-end Stock Price: $7.91
News: Relay Therapeutics Reports Third Quarter 2023 Financial Results and Corporate Highlights

Exscientia (EXAI): UP 21.5%; UP 14.4% YTD

Company Description:
- EXAI reported the first AI-designed drug candidate to enter clinical trials and has expanded its AI-based platform to develop novel therapeutic antibodies through generative AI design.
- Exscientia is collaborating with Bristol-Myers Squibb on a handful of drug candidates and has partnered with Sanofi, GSK and PathAI on drug discovery projects.
Market Capitalization: $773M
Month-end Stock Price: $6.10
News: Exscientia Business Update for Third Quarter 2023

Sub-Total:

Average November Stock Appreciation: UP 35.2%

Average YTD Stock Appreciation: UP 5.3%

Average Market Capitalization: $1.4B

Average Month-end Stock Price: $11.33

Absci Corporation (ABSI): UP x%; DOWN x% YTD

Company Description:
- is focused on antibody design, creating new from scratch antibodies (“de novo antibodies”), and testing them in laboratories in a 6-week process.
Market Capitalization: $197M
Month-end Stock Price: $1.67
News: Absci Announces Collaboration with AstraZeneca to Advance AI-Driven Oncology Candidate

BioXcel Therapeutics (BTAI): UP 36.9%; DOWN 20.5% YTD

Company Description:
- BTAI leverages existing approved drugs and/or clinically evaluated product candidates together with big data and machine learning algorithms to identify new therapeutic applications.
- Most of their drug candidates fit within the field of neuroscience disorders but the company’s pipeline also includes clinical studies for combining small molecule cancer drugs with an antibody pembrolizumab, for specific cancer cases.
Market Capitalization: $109M
Month-end Stock Price: $3.80
News: BioXcel Reports Q3 2023 Financial Results and Strategic Updates

e-therapeutics plc (ETXPF): DOWN 23.5%; DOWN 45.8% YTD

Company Description:
- is focused on developing in-silico new RNAi (RNA interference) therapies
Market Capitalization: $67M
Month-end Stock Price: $0.13
News: None

Lantern Pharma (LTRN): UP 47.4%; DOWN 34.6% YTD

Company Description:
- LTRN specializes in developing new classes of precision cancer drugs with novel mechanisms of action and “recycling” previously unsuccessful cancer drugs using machine learning algorithms, genomic data, and novel precision oncology biomarkers.
Market Capitalization: $39M
Month-end Stock Price: $3.95
News: Lantern Pharma Reports Third Quarter 2023 Financial Results and Operational Highlights

Evaxion Biotech A/S (EVAX): No Change in November; DOWN 55.1% YTD

Company Description:
- focus on infectious diseases and oncology.
Market Capitalization: $17M
Month-end Stock Price: $0.80
News: Evaxion to Unveil Potentially Groundbreaking AI-Immunology™ Precision Cancer Vaccine Concept

Sub-Total:

Average November Stock Appreciation: UP 31.0%

Average YTD Stock Appreciation: DOWN 67.3%

Average Market Capitalization: $85.6M

Average Month-end Stock Price: $2.07

Grand Total:

Average November Stock Appreciation: UP 34.5%

Average YTD Stock Appreciation: DOWN 21.5%

Average Market Capitalization: $740M

Average Month-end Stock Price: $6.70

In addition to the above small cap and primarily clinical-stage, companies there are 12 Big Pharma companies using AI to transform the landscape of drug discovery, clinical trials and manufacturing, namely: Moderna, Sanofi, Pfizer, Novartis, Janssen, Astra Zeneca, Bristol Myers, Bayer, Merck, GSK, Roche and Lilly. SourceMore By This Author:Top 10 Cyber Security Stocks Up 15% In November; Now Up 80% YTD
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