Top 50 Ai For Drug Discovery in Europe

The startup develops a drug discovery platform that combines artificial intelligence and synthetic biology to digitize natural diversity and identify potential drug candidates based on their functions. This approach aims to make the discovery of new therapeutics for cancer, metabolic diseases, and infectious diseases more predictable and cost-effective.

The startup offers a drug development platform that utilizes AI-driven asset diligence and valuation to enhance investment decision-making in biopharma. By providing a drug asset marketplace, the platform accelerates drug pipeline development and optimizes return on investment, enabling clients to effectively evaluate and present asset value during strategic planning and deal negotiations.

XtalPi is a pharmaceutical technology company that utilizes computational modeling and machine learning to predict drug interactions and properties, followed by experimental validation in wet labs. This approach reduces the time and cost associated with traditional drug discovery processes, enabling faster development of effective therapeutics.

The startup specializes in Pharma 3.0 drug discovery, utilizing advanced computational methods and machine learning algorithms to identify potential drug candidates more efficiently. This approach addresses the high costs and lengthy timelines associated with traditional pharmaceutical research and development.

Kuano's discovery platform integrates quantum mapping and AI-driven chemistry to enhance the design of enzyme inhibitors, significantly improving candidate quality and reducing development time. By addressing inefficiencies in drug discovery, Kuano enables pharmaceutical and biotech companies to identify superior starting points for drug candidates more rapidly.

This startup provides an AI-powered drug discovery platform that accelerates early-stage research by offering a screening and scoring tool. This platform helps pharmaceutical companies reduce the time and resources needed to identify and develop new drug candidates.

The startup has developed an artificial intelligence platform that predicts molecular properties and target affinity using structured perception and feature fusion techniques. This technology streamlines data analysis workflows for biopharma research teams, enabling them to conduct fewer experiments while improving drug discovery outcomes.

Totus Medicines utilizes AI/ML and DNA-encoded covalent library technology to discover small molecule drugs targeting previously undruggable disease pathways. Their platform enables high-throughput drug discovery, significantly increasing the speed and efficiency of developing therapeutics for advanced cancers and other serious conditions.

AIVIVO LTD develops artificial intelligence systems that generate multi-modal omics data to create OrganoMaps, which connect disease biology with treatment interventions at the organ level. This approach enables the development of targeted medicines for patients by providing insights into organ-specific disease mechanisms.

Qubit Pharmaceuticals utilizes quantum physics and advanced computational methods to develop precise drug candidates for complex diseases, focusing on oncology and inflammation. Their proprietary platform, Atlas, enables high-resolution drug design and molecular dynamics simulations, addressing the challenge of creating treatments for previously undruggable targets.

NEBULA utilizes dataset-free generative AI and physics-based modeling to create detailed 3D maps of macromolecules, facilitating the identification of potential drug targets. This technology enhances the drug discovery process by providing precise structural insights that are essential for developing new pharmaceuticals.

Helical provides an open-source platform that integrates DNA and mRNA Bio Foundation Models, enabling researchers and developers to leverage biological data for drug discovery applications. The platform facilitates target identification, biomarker discovery, and patient stratification, enhancing the efficiency and effectiveness of therapeutic development.

Bioptimus is developing a universal AI foundation model specifically for biological research, enabling researchers to leverage large-scale data analysis and predictive modeling. This technology addresses the challenge of slow and fragmented discovery processes in biomedicine by providing a cohesive platform for accelerating insights and innovations.

METiS Therapeutics utilizes artificial intelligence, machine learning, and molecular dynamics to develop targeted delivery systems for small molecules and gene therapies. The company addresses the challenge of efficient drug delivery by creating lipid nanoparticles that demonstrate high delivery efficiency and safety profiles across multiple organ systems.

The startup develops gene therapies using artificial intelligence to enhance the design and optimization of genetic constructs. Their technology enables researchers to create more predictable and controllable gene therapies, facilitating faster development for various applications.

Turbine's Simulated Cell™ platform utilizes AI to predict biological responses by simulating experiments across various models, including engineered cell lines and patient samples. This technology accelerates drug development by generating actionable insights in half the time of traditional methods, enabling researchers to identify optimal experimental pathways for novel cancer therapies.

CHARM Therapeutics utilizes its proprietary DragonFold technology, which employs 3D deep learning for protein-ligand co-folding, to develop small molecule inhibitors targeting previously undruggable proteins associated with cancer and other diseases. By addressing the challenge of the vast majority of the human proteome remaining undruggable, the company aims to create transformative therapies for patients with high unmet medical needs.

Abzu utilizes its proprietary explainable AI technology, the QLattice®, to analyze complex datasets in pharmaceutical research, providing transparent insights that enhance drug discovery and development. By delivering clear and interpretable models, Abzu addresses the challenge of opaque AI systems, enabling researchers to understand the underlying factors driving their data-driven decisions.

Healx utilizes generative AI to enhance and combine existing compounds for the rapid discovery of treatments for rare diseases, addressing the lack of effective therapies for over 90% of the 10,000 known rare diseases. By de-risking the drug development process, Healx increases the likelihood of successful outcomes and accelerates the delivery of new therapies to patients.

SilicoGene offers a no-code platform for health-tech teams to manage the entire AI lifecycle in drug discovery, enabling users to prepare bioinformatics data, train models, and deploy them efficiently. The platform provides on-demand access to cost-effective GPUs and ensures data security through HIPAA-compliant infrastructure, facilitating rapid deployment of AI solutions in real-world applications.

Nabla Bio utilizes biologically informed machine learning and experimental technologies to design antibodies with atomic precision, targeting complex disease mechanisms such as GPCRs and ion channels. The platform enhances drug manufacturability, safety, and efficacy by integrating AI-driven design with empirical measurement of human-relevant drug properties.

The startup has developed an artificial intelligence platform for biomarker discovery and sequencing analysis, focusing on extracting biosignatures from single-cell and multi-omics data. This platform enables physicians to efficiently analyze complex disease patterns, reducing data processing time and costs while enhancing insights for patient stratification.

Arctoris utilizes its automated laboratory platform, Ulysses™, to enhance drug discovery by improving data quality and experimental precision across various biological disciplines. The company addresses the inefficiencies in traditional R&D processes, enabling faster progression from target validation to candidate selection for biotech and pharmaceutical partners.

Kvantify develops a computational drug discovery platform that utilizes proprietary physics-based methods and bespoke algorithms to enhance the quality of compound screening for biotech and pharmaceutical companies. By integrating classical and quantum computing techniques, the platform accelerates research processes and reduces the risks associated with drug development.

The startup utilizes a biosynthetic mapping approach combined with experimental data analysis to identify microbial genes that can produce novel drug compounds. This enables pharmaceutical companies and research institutions to access unique molecules with potential new mechanisms for drug development.

Relation Therapeutics utilizes single-cell multi-omics, functional assays, and machine learning to identify druggable targets and develop therapies for severe diseases, starting with osteoporosis. By integrating high-resolution biological data directly from patient tissue, the company aims to enhance understanding of disease mechanisms and improve treatment outcomes.

PentaBind utilizes generative AI to design aptamers that can selectively target and bind to specific molecules, addressing the limitations of traditional wet-laboratory methods in therapeutic development. This technology enables the creation of highly effective aptamer drugs that are smaller, more selective, and capable of penetrating tumors, significantly improving the precision of cancer treatments.

Phenaros utilizes phenomics and AI-driven cell morphological profiling to accelerate drug discovery for rare diseases by automating data collection and experimentation. The company addresses the challenge of limited high-quality data in drug development, enabling faster and more efficient identification of new and repurposed therapeutics.

CoSyne Therapeutics utilizes polymathic AI and genetic computational systems analysis to expedite the development of pharmaceuticals targeting high-risk brain cancer. By digitizing cellular responses, the company enables medical researchers to create a new class of drugs more efficiently and at a lower cost than traditional methods.

Vincere Biosciences develops novel therapeutics targeting Parkinson's disease and age-related kidney disease, utilizing proprietary AI/ML tools to design and optimize drug candidates. Backed by over $2.7 million in grants from organizations like the NIH and the Michael J. Fox Foundation, the company aims to initiate human trials for its first molecule within 18 months.

The startup utilizes artificial intelligence to predict and manage drug shortages, ensuring timely access to essential medications. By analyzing supply chain data and demand patterns, the platform enhances inventory management for healthcare providers, reducing the risk of stockouts.

The startup develops analytics software that integrates multi-omic data with AI analytics to enhance drug candidate identification and biomarker discovery. This technology enables precision medicine organizations to streamline research processes and significantly reduce time-to-market for new treatments.

BioStrand utilizes its patented LENSai™ Integrated Intelligence Technology to analyze multi-omics data and accelerate antibody drug discovery through advanced computational methods. This technology addresses the inefficiencies in identifying and prioritizing lead drug candidates by providing rapid insights into drug interactions and predicted efficacy.

ALLOX utilizes systematic mutagenesis, high-throughput phenotyping, and biophysical modeling to identify allosteric switches in proteins, enabling the rapid development of novel therapeutics for human diseases. By harnessing genetics and AI, the company addresses the challenge of predicting and engineering protein functions, aiming to transform drug discovery and biotechnology.

BioStrand accelerates biotherapeutic discovery with its LENS ai™ platform, a SaaS solution that integrates multi-omics, textual, and structural data. Leveraging proprietary HYFTs® technology and advanced AI, including protein language models and vector search, the platform provides actionable insights to streamline target identification and optimize the drug discovery process.

Provides an AI-powered platform for analyzing complex medical data, including transcriptomics, epigenomics, and proteomics, using modular machine learning techniques. This system accelerates research by delivering fast, high-quality insights for identifying therapeutic targets, biomarkers, and disease mechanisms, reducing time and cost compared to traditional methods.

Poolbeg Pharma is a biopharmaceutical company focused on developing treatments for infectious diseases and metabolic conditions, utilizing artificial intelligence to enhance drug discovery efficiency. The company addresses the critical unmet medical needs in areas such as cancer immunotherapy-induced cytokine release syndrome and obesity through its robust pipeline of clinical assets.

This company develops AI-powered tools for drug discovery and medicinal chemistry, focusing on the early phases of drug development. Their platform offers molecular space search, property prediction, and interactive visualization to accelerate the identification and optimization of drug candidates.

SYDRA utilizes a closed-loop discovery platform that combines artificial neural networks with autonomous robotic in vivo screening to identify novel small molecules with geroprotective properties. The startup aims to develop longevity supplements and anti-aging drugs to combat the biological mechanisms of aging.

SyntheticGestalt develops machine learning models specifically designed for drug discovery, enabling researchers to identify potential drug candidates more efficiently. Their software automates various research processes, reducing time and costs associated with traditional drug development methods.

Molab is a biotechnology research company that utilizes an end-to-end discovery platform combining in-silico design, AI/ML methods, and wet lab validation to optimize small molecule drug development. Their technology accelerates hit-to-lead and lead optimization projects by 1.5 to 2 times through proven ADMET predictions and generative molecular design, addressing the inefficiencies in traditional drug discovery processes.

This startup provides an AI-powered platform that automates portfolio analysis and optimization for pharmaceutical and life science companies. It uses machine learning to conduct predictive analysis by integrating competitor, regulatory, demand, and supply data, enabling pharma managers to make data-driven business decisions and identify future opportunities.

Uses spatial proteomics combined with advanced microscopy, machine vision, and AI to map molecular interactions within biological samples at unprecedented resolution. This technology enables the identification of novel therapeutic targets and accelerates drug discovery, addressing the limitations of traditional methods in understanding complex disease mechanisms.

This startup develops AI solutions for life sciences, specializing in drug discovery. Their technology designs, conducts, and troubleshoots tasks to accelerate new medication development.

InterAx Biotech utilizes machine learning and mathematical modeling of cellular processes to generate datasets that predict drug effects on GPCR targets, addressing the challenge of cellular signaling in drug discovery. Their platform enhances hit-to-lead identification by providing tools for the discovery and optimization of high-efficacy drug candidates based on cellular biology mechanisms.

Ignota Labs utilizes its proprietary AI model, SAFEPATH©, to analyze bioinformatics and cheminformatics data, identifying mechanisms of drug toxicity and predicting their effects on human health. The company focuses on reviving promising drugs that have been shelved due to safety issues, accelerating their path to clinical trials and ultimately to patients.

The startup develops bioinformatics tools that utilize artificial intelligence for the identification of drug targets and the evolutionary analysis of protein sequences. By enabling the modeling and computational screening of mutant libraries, the company provides pharmaceutical firms with the capability to discover novel drug candidates in previously unexplored areas.

Dalton is developing an AI-driven platform for drug discovery that utilizes machine learning algorithms to predict molecular interactions and optimize compound selection. This technology addresses the inefficiencies in traditional drug design processes, significantly reducing the time and cost associated with bringing new pharmaceuticals to market.

Myria Biosciences develops GEMMS (Genetically Engineered Modular Molecule Scaffolds), a novel class of therapeutic molecules that integrates the properties of small molecules and biologics to create effective drug candidates. Utilizing computational data mining, synthetic biology, and high-throughput experimentation, Myria generates and tests millions of molecules to address unmet medical needs in diseases where existing treatments have failed.

The startup operates a biomimetic drug discovery platform that recreates micro-physiological conditions to enhance drug research efficiency. By providing reliable evaluations of drug efficacy and toxicology, the platform enables researchers to develop new therapies more cost-effectively, ultimately improving patient health outcomes.