HITS operates a physics-informed AI platform for pharma drug discovery, built on Python, Go, FastAPI, PostgreSQL, and Kubernetes on AWS. The tech stack and active projects reveal a company transitioning from public to private cloud while scaling ML infrastructure (Kubeflow, Airflow) to handle closed-loop lab automation and omics analysis—moving beyond virtual screening toward end-to-end experimental orchestration. Hiring velocity is accelerating with mid-level engineers and researchers, matching the complexity of their product commercialization and hyperlab service roadmap.
HITS is a South Korea-based AI biotech company that automates molecular discovery for pharma and research institutions. The platform combines physics-based modeling with deep learning for hit discovery, hit-to-lead, and lead optimization phases. HITS partners with academic centers, CROs, contract research organizations, and pharmaceutical companies including research divisions at major conglomerates and regional biotech firms. The product surface includes virtual screening, chemical reaction prediction, lab automation integration, and an omics analysis agent—all orchestrated through a cloud-native backend. Core pain points center on accelerating timelines and reducing costs in early-stage drug development, where traditional approaches are slow and capital-intensive.
Python, Go, FastAPI, PostgreSQL, Redis, Kubernetes (AWS EKS), Helm, ArgoCD, Argo Workflows, Kubeflow, Apache Airflow, React, Next.js, R, and Claude for AI modeling.
Active projects include closed-loop experimental orchestration, omics analysis AI agent, chemical reaction prediction and lab automation, hyperlab service enhancement, and a public-to-private cloud migration.
Other companies in the same industry, closest in size