From data to discovery: Five bioinformatics companies you should know in 2024

bioinformatics companies

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Bioinformatics is a field of science that integrates computer technology to analyze biological data, particularly DNA and protein sequences, to advance therapies and boost healthcare research. In this article, we take a look at five companies at the top of their bioinformatics game in 2024.

Table of contents

    Synthace

    British-American life science company Synthace is a digital experiment platform that helps people plan, run, and analyse automated experiments. Included in this is DNA Assembly, and at the start of the workflow, Synthace allows scientists to import assembly design files at the start of the workflow, after which, it helps create the plasmids – circular DNA molecules found in microbes – that are required for projects.

    Once customers specify what kind of plasmid they want designed, Synthace’s platform calculates the liquid movements, concentrations, and dilutions needed throughout the entire process. 

    The company helps estimate reagent volumes as well as automation instructions for customers to be able to run experiments without having to use spreadsheets or any kind of programming. 

    The bioinformatics company aids scientists in planning and automating processes like DNA assembly for their research and development (R&D) projects.

    Synthace has previously worked with tech giants like Microsoft to design manufacturing processes for lentiviral vectors to be able to program cells to discover new medicines. It has also collaborated with global biopharma Ipsen to develop medicines to treat rare diseases and pain relief.  

    The company has also been known to use OpenAI’s ChatGPT to design complex experiments.

    Last year, Synthace was among 50 companies like Google, mental healthcare company Headspace, and food company Beyond Meat, to win the international Impact Fifty award recognizing the most impactful companies to work for.

    Verge Genomics

    Immersed in the drug discovery space, Verge Genomics leverages artificial intelligence (AI) to discover and develop drugs. Its CONVERGE discovery platform is a closed-loop machine learning system that combines genomics and computational tools to scout for drug targets and predict how well investigational medicines could perform in the clinic.

    This way, it looks to develop drugs at a faster rate. In its pipeline, Verge has an amyotrophic lateral sclerosis (ALS) drug undergoing proof-of-concept studies. ALS is a fatal motor neuron disease that affects the nerve cells in the spinal cord and brain. The drug candidate VRG50635 is a small molecule PIKfyve inhibitor that was entirely discovered and developed using its AI-enabled platform. 

    In the discovery stage, it is developing drugs to treat Parkinson’s disease, schizophrenia, psoriasis, atopic dermatitis, Crohn’s disease, and ulcerative colitis. It also has a partnered ALS program with pharma giant Lilly.

    Late last year, it joined forces with Massachusetts-based rare disease therapeutics company Alexion Pharmaceuticals – a subsidiary of multinational AstraZeneca – to identify drug targets for rare neurodegenerative and neuromuscular diseases. The deal would see Verge receive up to $42 million in upfront and near-term payments, and a total of up to $840 million in milestone payments in the future.

    VeriSIM Life

    Focused on drug discovery and development, California-based VeriSIM Life has a hybrid AI platform aimed at bringing therapies all the way from the lab to hospitals to treat patients.

    As 90% of drug candidates are said to fail in the clinic, the BIOiSIM platform is designed to predict whether a drug could hit a dead-end or succeed on the market. This drug development decision engine produces a “credit score” for a drug’s potential. It not only analyzes the efficacy but also possible side effects and toxicities that could be associated with these drugs and species variability.

    With the help of AI, it looks to reduce waste and lower costs – by $3 million to be precise – by improving accuracy and scalability.

    Its AtlasGEN drug designer, which was launched in April, helps identify new molecules based on desired chemical properties. Its molecular search space is supported by more than a trillion compounds. The platform predicts the protein-binding abilities by using machine learning and is said to be 72% more efficient than molecular docking-based approaches, which are a set of grid-based methods to understand how a ligand would interact with a receptor.

    Last year, the bioinformatics company partnered with analytics company Clarivate to accelerate R&D and set in motion the latter’s drug discovery platform that provides insights into the safety and efficacy of molecules.

    Recursion

    American drug developer Recursion uses its mapping and navigating technology to explore areas in disease biology to identify new medicines. These maps are built using image-based high-dimensional data that are generated in-house.

    On a weekly basis, Recursion conducts up to 2.2 million experiments in labs to capture cell responses to whole genome knockouts. 

    Scientists at Recursion begin by taking into account a particular therapeutic field and going through datasets of genes and drug targets. By performing gene knockouts followed by cell imaging and then harnessing machine learning, it is able to gather data on the genes and processes associated with a certain disease.

    The Recursion Operating System (OS) has been key to creating the biotech’s therapeutic portfolio. One of its most advanced candidates is a phase 2 drug for the treatment of cerebral cavernous malformation, a condition where small blood vessels abnormally collect in the central nervous system. The small molecule REC-994 was found to be well-tolerated in phase 1 trials.

    It is also evaluating its drug REC-2282, a small molecule pan-HDAC inhibitor for NF2-mutated meningiomas, a type of brain tumor that has no FDA-approved drugs as of yet. It was also well-tolerated, and unlike other HDAC inhibitors, it reduced cardiac toxicity, according to phase 1 trials.

    Last month, it announced the pricing of $200 million in a public offering. It also built the NVIDIA DGX SuperPOD AI supercomputer back in May, in a bid to improve drug discovery efforts.

    Twist Bioscience

    California-based company Twist Bioscience is a major player in bioinformatics, with its array of services ranging from the sale of gene fragments and clonal genes to creating complex libraries that are based on customer requirements.

    A list of variants is generated at first, and each is then screened to remove variants that may not succeed in development – boosting the chances of accuracy in drug development. If variants contain restriction sites, codons are changed to avoid creating restriction sites. Twist’s DNA synthesis platform is then employed to print each variant. The DNA is then amplified to create the library, which is validated by next-generation sequencing. 

    Last month, it reported the discovery of TB206-001, an antibody designed to improve cancer treatment, through its silicon platform. Twist has had more than 400 successful monoclonal antibody discovery campaigns and has collaborated with over 200 life science companies to advance its technologies, including Japanese multinational Takeda, German giants Bayer, and Boehringer Ingelheim.

    Bioinformatics market poised to skyrocket 

    The bioinformatics industry has a market value of $16.3 billion at present and is forecasted to reach nearly $24 billion in five years, according to a report by Mordor Intelligence. These days, most biotechs rely on bioinformatics in drug discovery to improve their chances of viable candidates making it to the clinic. And so, many have a bioinformatics wing that collects and assesses drug target data. 

    Big multinationals like DNA giant Illumina, German molecular diagnostics company Qiagen, and drug developer Thermo Fisher Scientific are among those that have robust bioinformatics services. And in recent times, the integration of AI has brought about a significant change in how data is analyzed and interpreted, so as to boost healthcare research and development.

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