Uncover the Impact: Pharma’s AI-driven Leap in Drug Development. Explore 10 Leading AI Startups Shaping Drug Discovery in 2023 with Innovations across Small Molecule Therapeutics, Molecular Generation, and Modeling – A Data-Driven Overview.

Leveraging the Big Data & AI-powered StartUs Insights Discovery Platform, which encompasses over 3,790,000 startups and scaleups globally, we’ve pinpointed 491 AI startups at the forefront of drug discovery. Delve into our Global Startup Heat Map to spotlight the 10 AI-driven drug discovery startups to keep an eye on in 2023. Additionally, discover the geographic distribution of all 491 startups and scaleups analyzed, showcasing significant activity in the US, closely followed by Western Europe. These pioneering startups are actively engaged in diverse solutions, spanning convolutional graph networks, molecular modeling, protein-drug interactions, and agile drug development.

As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant technologies and industry trends quickly & exhaustively. Based on the data from the platform, the Top 5 AI-powered Drug Discovery Startup Hubs are in London, New York City, Cambridge, Boston & San Francisco. The 10 hand-picked startups highlighted in this report are chosen from all over the world and develop solutions for small molecule therapeutics, in vivo guided discovery, druggability prediction, and more.

10 Top AI Startups advancing Drug Discovery in 2023

Innovations in AI-powered drug discovery enable faster and more efficient drug development. For instance, machine learning algorithms analyze vast amounts of biological data and identify potential drug candidates. These algorithms also identify patterns and relationships in data that are difficult for humans to detect, enabling more precise and targeted drug development. Another recent innovation in AI-powered drug discovery is the use of virtual screening tools that simulate the interaction between potential drug candidates and target molecules. This method reduces the time and costs associated with traditional drug discovery methods. Additionally, AI-powered clinical trials allow pharma companies to collect and analyze patient data more efficiently and accurately. This approach helps them identify patient subgroups that may benefit from a particular drug, enabling more personalized medicine approaches.

• Ailynix – Convolutional Graph Networks for Drug Design
• Pangea Botanica – Novel Small Molecule Therapeutics
• DevsHealth – Molecular Modeling
• Vevo Therapeutics – In Vivo Guided Drug Discovery
• Gandeeva Therapeutics – Protein-Drug Interactions
• Cortex Discovery – Molecular Dynamics
• CardiaTec Biosciences – Cardiovascular Disease Drug Targets
• Boltzmann Labs – Novel Small Molecule Discovery
• molab.ai – Absorption, Distribution, Metabolism, Excretion, & Toxicity (ADMET) Prediction
• CarbonSilicon – Druggability Prediction

Ailynix Innovates with Convolutional Graph Networks (CGNs) in Drug Design

Ailynix, a US-based startup, specializes in the realm of AI-driven drug design and discovery by harnessing the power of deep learning and Convolutional Graph Networks (CGNs). Employing supervised training methods, the company develops quantitative structure-activity relationship (QSAR)-based computational models for predicting chemical structures. Ailynix excels in identifying potential drugs from an extensive molecule database, facilitating subsequent computational searches and refinement processes. Their platforms play a pivotal role in advancing protein-based therapeutic drug discovery, utilizing orthosteric, allosteric, and functional data. The company’s clientele spans biotech and pharma companies, contract research organizations (CROs), and university research labs.

Pangea Botanica Unveils Cutting-Edge Small Molecule Therapeutics

Pangea Botanica, a startup based in the UK, introduces PangeAI, a state-of-the-art AI-powered platform designed to expedite drug discovery and development. Specializing in mapping the chemical composition of plants, Pangea Botanica’s platform creates an extensive dataset of natural products, facilitating precise compound matching. The platform not only predicts chemical properties but also identifies modes of action and synergistic effects. By seamlessly integrating AI, metabolomics, and cheminformatics, PangeAI streamlines the scalable discovery of nature-inspired therapeutics. Furthermore, it plays a crucial role in proposing lead candidates for development, thereby elevating the advancement of both new and existing compounds

DevsHealth: Advancing Molecular Modeling

• • Spanish startup utilizing AI and molecular modeling for antiviral and antibiotic treatments.
  • AI optimizes drug design, predicts side effects, and forecasts ADME properties.
  • Integrates public-source databases for streamlined handling of massive datasets in gene expression experiments, bioactive compounds, and proteins.
  • Leverages real-world data (RWD) and quantum computing for enhanced AI models, improving anti-infective treatments.

Vevo Therapeutics: In Vivo Guided Drug Discovery

• • • US-based startup developing Mosaic, a platform for high-resolution, single-cell in vivo data generation at scale.
    • Measures phenotypic and transcriptomic changes in cell states to capture drug efficacy rules.
    • Utilizes proprietary methods for pooling cells from multiple patients in one tumor and single-cell RNA profiling.
    • Studies drug-cell interactions in vivo, uncovering mechanisms of action and resistance overlooked by current in vitro models.

Gandeeva Therapeutics: Capturing Protein-Drug Interactions

• • Canadian startup offering an AI and cryogenic electron microscopy-based drug discovery platform.
  • Modules include SPOTLIGHT for target identification, HYPERFOCUS for mapping druggable sites, and CRYO-CADD for structural insights.
  • Combines chemistry, biology, imaging, and machine learning for high-speed and high-resolution capture of protein-protein and protein-drug interactions.

Cortex Discovery: Advancing Molecular Dynamics

• • German startup providing deep learning-based solutions for accurate simulations of ligand-protein binding.
  • Technology models chemical processes of interactions between targets and drug-like molecules.
  • Predicts on-target interactions, off-target interactions, and drug metabolism and toxicity, finding applications in life extension and age-related disorders.

CardiaTec Biosciences: Cardiovascular Disease Drug Targets

• • UK-based startup applying AI and large-scale multi-omics data for developing cardiovascular disease drug targets.
  • AI-driven multi-omics analysis platform uncovers relationships across gene variation, methylation, expression, proteomics, and metabolomics.
  • Features patient stratification and biomarker identification for personalized medicine, improving patient care.

Boltzmann Labs: Novel Small Molecule Discovery

• • Indian startup with a chemistry studio, BoltChem, for discovering novel small molecules using generative AI.
  • Creates QSAR property models using machine learning and deep learning.
  • AI-based synthesis planning tool, ReBolt, simplifies reaction pathway design.
  • BoltBio, a target identification platform, uses multi-omics analysis, knowledge graphs, and neural networks to accelerate treatment for rare and common diseases.

molab.ai: ADMET Prediction Engine

• • German startup advancing drug and compound discovery through ADMET predictions and a compound optimization suite.
  • Prediction engine provides highly accurate ADMET property predictions with reliable confidence indicators.
  • Performs robustly in unfamiliar chemical space, outperforming physics-based models and other AI solutions.
  • Compound optimization suite offers suggestions for novel and alternative molecular structures for better binding affinity and synthetic accessibility.

CarbonSilicon: Druggability Prediction

• • Chinese startup offering a drug discovery workflow leveraging AI-generated content, self-supervised pre-training, reinforcement learning, and physics-based modeling.
  • Inno-Docking provides complete protein and ligand preparation with intelligent docking parameter settings.
  • Inno-Rescoring features AI-scoring functions to evaluate protein-ligand binding affinity.
  • Comprehensive druggability assessment involves Inno-ADMET, ChemFH, and Inno-SA modules for ADMET properties, hit compound filtering, and substructure-related toxicity prediction.

Discover All Emerging Pharma Startups: The showcased startups are just a glimpse of the innovative pharma landscape. Download our free Pharma Innovation Report for a comprehensive overview or reach out for in-depth research on the latest technologies shaping the industry in 2023!”