Drug discovery using Open-source tools

About this event

Modern drug discovery is increasingly powered by open‑source software, machine learning, large datasets, and computational modeling. In this special PyData Singapore session, we explore how open tools are accelerating innovation in biology, chemistry, and pharmaceutical research. PyData Singapore is a community for users and developers of open‑source data tools — Python, R, Julia, and beyond — who gather to share ideas and learn from each other. What this meetup is about We’ll dive into how open‑source technologies are used in real drug discovery workflows, including: • AI/ML for target identification, compound screening, and lead optimization • Molecular simulations, docking, and generative chemistry • Open datasets for genomics, proteomics, and structural biology • Statistical and mathematical models for ADMET and toxicity prediction • Reproducible pipelines, cloud computing, and workflow automation • Collaboration between industry, academia, and open‑source communities Speakers We’re bringing together 3–4 experts from biotech companies (Hummingbird Bioscience (https://hummingbirdbioscience.com/)), AI startups (LynxKite (https://lynxkite.com/)), research institutes, and open‑source projects. Each will share practical insights, tools, and case studies from their work in computational drug discovery. Who should attend • Data scientists & ML engineers • Computational chemists & biologists • Bioinformatics researchers • Pharma & biotech professionals • Students and anyone curious about open‑source science Why this matters Drug discovery is becoming more open, more computational, and more collaborative. Whether you’re working in life sciences or simply interested in applied machine learning, this session will show how open‑source tools are reshaping the future of therapeutics.