9 Computational Biology Trends in 2023

Are you curious about which computational biology trends & startups will soon impact your business? Explore our in-depth industry research on 494 computational biology startups & scaleups and get data-driven insights into technology-based solutions in our Computational Biology Innovation Map!

Developments in high-performance computing and high-throughput hardware are advancing emerging computational biology trends. These trends are enabling novel drug development, nutrition analytics, and cancer therapies. Moreover, AI, machine learning, and high-performance computing allow large-volume data processing combining biological information from multiple sources for higher accuracy testing and modeling.

Innovation Map outlines the Top 9 Computational Biology Trends & 18 Promising Startups

For this in-depth research on the Top Computational Biology Trends & Startups, we analyzed a sample of 494 global startups & scaleups. The result of this research is data-driven innovation intelligence that improves strategic decision-making by giving you an overview of emerging technologies & startups in the computational biology industry. These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform, covering 2 500 000+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant startups, emerging technologies & future industry trends quickly & exhaustively.

In the Innovation Map below, you get an overview of the Top 9 Computational Biology Trends & Innovations that impact 400+ companies worldwide. Moreover, the Computational Biology Innovation Map reveals 18 hand-picked startups, all working on emerging technologies that advance their field.

Top 9 Computational Biology Trends

  1. Genomics
  2. Drug Development
  3. Proteomics
  4. Biological Modeling
  5. Metabolomics
  6. Bioinformatics
  7. Transcriptomics
  8. Big Data
  9. Machine Learning
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Tree Map reveals the Impact of the Top 9 Computational Biology Trends

Based on the Computational Biology Innovation Map, the Tree Map below illustrates the impact of the Top 9 Computational Biology Trends. Developments in genomics are enabling a range of solutions with faster and cheaper DNA sequencing and synthesis. Computational tools are also addressing bottlenecks in drug development, reducing time and costs. Machine learning-based solutions automate processes across biotech, pharma, and other life sciences sectors. Advances in omics technologies, such as proteomics, transcriptomics, and metabolomics, enable develop of rich phenotypes. Lastly, startups leverage biological modeling to virtually recreate living organisms and processes.

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Interested to explore all 490+ computational biology startups & scaleups?

 

Global Startup Heat Map covers 494 Computational Biology Startups & Scaleups

The Global Startup Heat Map below highlights the global distribution of the 494 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform, the Heat Map reveals that the US sees the most startup activity.

Below, you get to meet 18 out of these 494 promising startups & scaleups as well as the solutions they develop. These computational biology startups are hand-picked based on criteria such as founding year, location, funding raised, & more. Depending on your specific needs, your top picks might look entirely different.

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Top 9 Computational Biology Innovation Trends for 2023

1. Genomics

Sequencing, mapping, and editing genomes play an important role in the development of a range of solutions across biotech, pharma, and life sciences. However, high computational time and costs limit the use of genomics. To overcome this, startups are developing genome sampling and analysis solutions that provide faster and better quality data for sequencing. Moreover, advances in genomics are enabling personalized treatment and care.

Reveal Genomics offers Gene Expression Analysis

Spanish startup Reveal Genomics develops diagnostic tools to determine therapeutic options for patients with cancer. Its genomic tool, HER2DX, identifies relapse risk, probability of therapy response, and tumor expression levels in early-stage HER2+ breast cancer. To do this, it measures and analyses the expression of 27 different genes. This allows clinicians to predict the benefits of targeted cancer therapies and reduce cancer recurrence.

Wobble Genomics advances Nucleic Acid Sequencing

UK-based startup Wobble Genomics advances nucleic acid sequencing for biomarker discovery. Its Level-Up library normalization technology increases sampling efficiency during sequencing. Moreover, it creates a uniform distribution of unique sequences in cDNA libraries, maximizing non-targeted sequencing efficiency. This way, the solution offers both increased sensitivity and accuracy.

2. Drug Development

Improvements in drug development processes continuously reduce the time to go from a drug candidate to a product to market-ready products. Speed is especially important in cases such as the development of the COVID-19 vaccine. Besides this, the process of drug development requires intensive research, analytics, and clinical trials while meeting regulatory compliance. This results in lengthy and complex development processes with high uncertainty in cost. To alleviate these problems, startups are developing computational platforms that assist in end-to-end drug development.

PeLeMed provides Mutation Mediated Drug Resistance Platform Technology

South Korean startup PeLeMed develops small-molecule drugs for oncological and viral diseases. Its kinase-targeted precision drug design platform (PeLeSeLect) features kinase active conformation, targets regions with low risk of mutation, and has a patentable compound library. Additionally, its AI-applied high-efficiency drug discovery platform (AHEDD) uses deep learning to analyze the interaction sites between target proteins and compounds. Besides this, it also features AI-based predictions of drug-target interactions and designs optimized drug structures. PeLeMed’s platforms greatly reduce the time to design and validate compounds for drug development.

GlycoEra advances Protein Engineering-based Therapeutics

Swiss startup GlycoEra develops a protein engineering and production platform to treat autoimmune, oncology, neurology, and infectious diseases. The CustomGlycan platform engineers complex carbohydrate structures and target the degradation of circulating and membrane proteins that cause disease. It activates the biology of naturally occurring scavenging receptors while avoiding undesirable immunogenicity from aberrant glycan structures. The platform’s proprietary modular genetic toolbox greatly reduces the time for drug development while keeping manufacturing processes cost-effective.

3. Proteomics

Proteomics enables researchers to analyze the entire protein complement of cells tissues and organisms. Current protein analysis solutions face a wide range of limitations such as high complexity in analysis, false positivity, low reproducibility, and much more. Besides this, the high computational requirements to process protein data make it uneconomical for most research purposes. Startups use advances in computing and algorithms to create software solutions that assist in identifying biomarkers and treating rare diseases through proteomics.

Protai advances Molecular Level Disease Simulation

Israeli startup Protai develops a proteomics platform that simulates diseases on the molecular level using AI and proteomic data. Its AI-based drug discovery platform Protai Atlas harmonizes large clinical datasets and healthy samples from various organs and indications. This also serves as a foundation for pattern recognition of specific causal targets to modulate. Besides this, its machine-learning model identifies up to 1,000,000 proteo-forms including post-translational modifications, protein-protein interactions, and more. Lastly, the platform contains a target prioritization engine to predict targets that are the best candidates for further drug development. This enables the development of drugs and therapeutic treatments for complex diseases using small molecules.

Diag-Nose Medical offers a Clinical Decision Support System

Australian startup Diag-Nose Medical develops a clinical decision support system specifically for rhinologists. The platform provides actionable clinical insights and biological data about a patient’s sinonasal inflammatory profiler through proteomics and machine learning that generate objective biological information. Diag-Noses’s technology also samples higher volumes than traditional swabs or lavage techniques, is noninvasive, and uses localized biomarker analysis to reduce inaccuracy. This enables medical professionals to accurately perform differential diagnosis, treatment monitoring, and assess disease progression.

4. Biological Modeling

The applications of Biological Modeling range from simple data visualization to highly complex protein folding simulations. Moreover, as the complexity of modeling increases so too does the computational resources, time and cost. This is why startups are developing biological modeling software solutions that use advanced algorithms, high-performance computing, cloud computing, and more. This broadens the scope for research and development in therapeutics, drug development, genetics, etc.

Helico creates Plant-based Pharmaceutical Compounds

New Zealand-based startup Helico creates proteins, antibodies, and enzymes in host plants through advanced computational science and plant biology. Its production process features proprietary computational algorithms trained on biological data that speed up genetic work in plants. Moreover, the computational models are validated with targeted experiments to increase the speed of compound discovery. The use of computing in compound discovery and development also serves to greatly reduce the need for experimentation on living organisms to achieve the desired outcome.

Lingyi Biotechnology develops a Rare Disease Modeling Platform

Chinese startup Lingyi Biotechnology develops a rare disease animal model platform to treat genetically rare diseases. The platform is built on a series of rare disease mouse and rat models that serves to accurately reproduce disease phenotype in humans. Additionally, the platform is also designed to model mutant strains as well which is a bottleneck in rare disease drug development processes.

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5. Metabolomics

Metabolomics involves the large-scale study of small molecules within cells, biofluids, tissues, and organisms and their interactions. The advances in sensors and computing in metabolic analysis improve metabolic tracking, biomarker identification, drug screening, drug development, and more. However, the metabolome is sensitive to various genetic and environmental stimuli which therefore limits its analysis to laboratories. Moreover, the sensitivity also results in subtle variations which affect research and analysis outcomes. To alleviate these problems, startups are developing precise and accurate metabolomics-based platforms and devices that provide highly accurate health data.

ViBo Health develops Personalized Predictive Healthcare

US-based startup ViBo Health creates a smart health-tracking solution that uses metabolic measurements. Its scanner trackers a person’s health status through their metabolic process rather than only vital signs. Additionally, their solution combines magnetic resonance spectroscopy with advanced signal processing and machine learning to understand metabolomic data and provide recommendations. Besides metabolomics, ViBo’s health scanners provide health and fitness insights using data on the vitamin, cholesterol, and diabetes levels. Moreover, their health tests are useable multiple times a week for predictive healthcare recommendations.

Interface Biosciences develops an Inflammatory Disease Therapeutics Platform

US-based startup Interface Biosciences builds a metabolomics-based therapeutics platform for inflammatory diseases. The platform uses culturomics to grow microbial isolates, metabolomics of microbial supernatants to create microbial natural products, and systems immunology for screening. Moreover, Interface Bio’s hybrid experimental and computational pipeline features novel chemical libraries optimal for machine learning-based feature selection. Their solution uses advances in computing, AI, and machine learning to explore the microbial chemical space for small-molecule drug development.

6. Bioinformatics

Bioinformatics is a field that primarily focuses on genome sequencing to analyze variation and expression, predict protein structure and function, and so on. Moreover, studying genes and proteins requires a tremendous amount of computational power. Additionally, the development of accurate models requires combining data across multiple vast databases which is a time and resource-intensive task. This is why startups are developing solutions such as cloud computing which removes the need for on-premises hardware for researchers. Besides this, the advances in machine learning, AI, and computing enable startups to create high-speed biological computation platforms.

PipeBio provides a Cloud-based Bioinformatics Platform

Danish startup PipeBio develops a cloud bioinformatics platform for antibody and peptide screening and drug development. It contains a range of features such as sequence editing and annotation, sample clustering and comparison, database management, workflow automation, and detailed reports. The cloud-based platform enables scientists to perform phage display panning, automate hybridoma analysis, and much more. In this way, scientists and researchers are able to perform computationally intensive tasks from anywhere and at any time through cloud computing.

Congruence designs Novel Small Molecules

Canadian startup Congruence offers computational and experimental small molecule drug discovery. The startup develops treatments for rare diseases with structural bioinformatics, computational chemistry, and machine learning. Its in-silico platform enables the rapid and scalable design of pharmacological stabilizers with a validated class of small molecules to treat diseases of protein misfolding.

7. Transcriptomics

Transcriptomics assists scientists and researchers to understand how genes are expressed and interconnected. Moreover, advances in transcriptomics allow for the identification and quantification of up to ten thousand RNA and protein species in tissues. Comparatively, previous conventional methods such as PCR and western blotting provided little information. Moreover, the increasing prevalence of transcriptome and proteome analysis solutions is overcoming the technical and computational limitations of biological research.

Alithea Genomics specializes in Blood Transcriptomics

Swiss startup Althea Genomics develops highly scalable, multiplexed RNA sequencing. Their technology is based on highly optimal sets of barcoded primers called BRB-seq oligos which tag individual RNA samples in a unique and sample-specific manner. Moreover, the startup’s custom globin blockers integrate with existing BRB-seq workflow which results in better blood transcriptomics studies based on gene expression analysis.

Resolve Biosciences advances Multiplex Spatial Analysis

German startup Resolve Biosciences develops a technology platform for multi-analyte, highly multiplex spatial analysis. Its Molecular Cartography Technology creates deep contextual data sets that illuminate molecular interactions at subcellular resolution while retaining sample tissue. The platform provides a high-resolution view of transcriptomic activity and gives the ability to interrogate hundreds of genes in a single run. It enables scientists and researchers to find solutions to difficult problems in fields such as oncology, neuroscience, and infectious disease.

8. Big Data

The amount of scientific, biological, and health data generation is increasing each year with gene sequencing data alone exceeding several petabytes. While the availability of more data improves the accuracy of analysis and models, it also requires significantly more computing resources. Additionally, data structures are not uniform across different databases and may lead to incorrect analysis or anomalies when combined. To overcome this, startups are developing text mining, algorithms, etc. that allow large-volume data analysis even across multiple disparate data sources.

TenWise develops a Text Mining Platform

Dutch startup TenWise specializes in text mining that uses AI-powered biological interpretation of in-house data. Its KMAP platform automates data collection of scientific literature by scanning through PubMed’s data including biological keywords, gene descriptions, metabolites, and more. The platform also applies precompiled filters called BioSets which contain a curated list of topics. The platform’s big data text processing finds improves workflows in the fields of research for human genes, micro-organisms, metabolites, pathways, phenotypes, diseases, and more.

Alike develops AI-based Medical Record Analysis

Israeli startup Alike develops digital healthcare analytics solutions to monitor patient health conditions. Its solution features a proprietary algorithm that computers similarities between two individuals and provides an Alikeness score. The computational algorithm uses cluster analysis to find similarities based on a wide range of conditions and characteristics and provides better-personalized treatments. Besides this, the analysis uses AI, crowdsourcing, and big data to derive insights from medical health profiles. In this way, using the vast amount of electronic health records patients are able to connect with others who have similar health issues and share their medical journeys.

9. Machine Learning

Machine learning provides the most cost-effective tools for building predictive models, annotating new genomic sequences, identifying important sites in proteins, and more. Advances in sensors and analytical devices provide vast amounts of data in short timeframes. However, deriving insights and understanding the data requires a significant investment of time and effort. To overcome this problem startups are training and incorporating machine learning in biological data analysis. The machine learning algorithms are able to quickly identify patterns and make connections in data and provide comprehensive reports within seconds.

InsectSense advances VOC Fingerprinting

Dutch startup InsectSense develops solutions to detect and fingerprint volatile organic compounds (VOCs). Its LumiNose is a molecular biotechnology device that uses a microarray chip with the sensitivity of insect smell senses. Moreover, LumiNose integrates machine learning to fingerprint volatile organic compounds from complex mixtures of samples. InsectSense’s biochip, fingerprint database, and reader box products together enable the accurate diagnosis of plant and animal diseases.

Avalo accelerates Crop Evolution

US-based startup Avalo develops an AI-based platform to identify important crop traits. Its platform uses a patented GDIP method to identify gene regions associated with traits of interest. Following this, a diagnostic assay predicts the performance of new crop varieties. The platform uses advanced predictive evolution and in silico optimization to create new crop varieties with its AI learning from each new gene discovery and crop creation project. In this way, through gene analysis, crops are able to be provided with the traits for resilience to climate change and provide higher yields.

Discover all Computational Biology Trends, Technologies & Startups

Drug development and bioinformatics have seen significant developments in recent years due to the COVID-19 pandemic. While advances in machine learning, AI, high-performance computing, and big data processing enable more applications of computational biology beyond laboratory environments. Lastly, advanced algorithms and biological modeling software are creating novel therapies for hard-to-treat diseases such as cancer.

The Computational Biology Trends & Startups outlined in this report only scratch the surface of trends that we identified during our data-driven innovation & startup scouting process. Among others, high-throughput sequencing, systems modeling, and single-cell analysis will transform the sector as we know it today. Identifying new opportunities & emerging technologies to implement into your business goes a long way in gaining a competitive advantage. Get in touch to easily & exhaustively scout startups, technologies & trends that matter to you!

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