Drugparadigm is an AI research startup focused on accelerating the discovery and development of next-generation therapeutic modalities. We build and apply advanced deep learning models that help researchers design, evaluate, and prioritize novel drug candidates across multiple modern therapeutic modalities.

Our work spans three core discovery and therapeutic focus areas:

1.⁠ ⁠Targeted Protein Degradation: We develop deep learning models for degradation-based modalities such as PROTACs and molecular glues, supporting rational design, complex formation, permeability optimization, and prediction of degradation potential.

2.⁠ ⁠Targeted Drug Delivery (Oncology Therapeutics): We build predictive deep learning models for delivery-based modalities such as ADCs and PDCs to improve targeting precision, manage toxicity, optimize drug-to-antibody ratio (DAR), and prioritize effective therapeutic constructs.

3.⁠ ⁠⁠Affinity-Centric Molecular Design: We design and evaluate both small and large molecules using AI-driven, pocket-aware and structure-guided approaches, along with advanced generative models for macrocyclization, to improve binding affinity, specificity, and functional inhibition across complex therapeutic targets.

We offer a suite of research-grade deep learning models that operate directly on standard molecular representations, including SMILES strings, SDF/PDB structure files, and FASTA protein sequences. These models can be used as standalone components for specific prediction or generation tasks, or integrated directly into existing drug discovery pipelines and workflows without requiring specialized hardware or complex infrastructure.

Whether you are an independent researcher, a biotech startup, an academic or industrial research lab, or a large pharmaceutical organization, Drugparadigm supports faster experimentation, informed decision-making, and systematic exploration across complex therapeutic modalities.

Drugparadigm

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