AutoDrug | Precision Medicine Through Computational Innovation

Core Technology

Platforms built for molecular precision

AutoDrug's proprietary platforms represent a paradigm shift in drug discovery, enabling rapid development of novel compounds with optimal target binding and pharmacological properties.

AutoGen Platform

Our compound generation platform (Registration No.: 2021SR1328859) designs novel molecular scaffolds that transcend traditional drug design limitations. By combining deep learning with molecular mechanics, we generate compounds with unprecedented specificity and reduced off-target effects.

AquaTrace Engine

Our proprietary AquaTrace engine identifies and evaluates key "hot" water molecules within a protein's binding pocket. By providing critical insights into the hydration landscape, AquaTrace guides the strategic design of molecules that can displace these water molecules to achieve superior binding affinity and selectivity, transforming challenging targets into druggable assets.

ADMET Predictor

Advanced machine learning models predict Absorption, Distribution, Metabolism, Excretion, and Toxicity properties before synthesis. Our models, trained on over 2 million compounds, achieve 95% accuracy in predicting drug-like properties.

Quantum-Accurate AI Scoring

We have integrated advanced AI methods to predict protein-ligand interaction potentials with the accuracy of quantum chemistry. This allows for the rapid and precise evaluation of binding energies at a fraction of the computational cost, dramatically improving the speed and reliability of our virtual screening and lead optimization campaigns.

Services

From target to clinical candidate

From target identification to clinical candidate optimization, AutoDrug provides comprehensive computational drug discovery solutions tailored to your therapeutic goals.

Lead Discovery & Design

Transform your targets into clinical candidates through our systematic computational approach. We deliver multiple design vectors with synthesis-ready compounds in weeks rather than months, dramatically accelerating your discovery timeline.

Hit-to-Lead Optimization

Transform early hits into potent lead compounds through our water molecule-based optimization approach, improving potency while maintaining or enhancing ADMET properties. Our process requires significantly fewer synthesis cycles while achieving greater potency gains.

Virtual Screening

Screen our multi-billion compound virtual library against your targets to identify novel chemical starting points with exceptionally high hit rates and structural diversity, providing multiple vectors for development with strong IP positions.

Computational Modeling Services

Access our advanced molecular modeling platforms for sophisticated evaluation of compound-target interactions, cryptic binding site identification, and structure-based drug design support, enhancing your internal discovery efforts.

Success Stories

Timelines compressed, candidates delivered

Our technology platforms have consistently delivered exceptional results across multiple therapeutic areas, transforming discovery timelines and outcomes.

01

POLQ Inhibitor: De Novo Design to Clinic in Record Time

Leveraging our integrated computational platform and proprietary virtual compound library, we achieved the de novo design of multiple chemical series with novel scaffolds targeting POLQ. This entire discovery phase, from target analysis to the delivery of patentable lead compounds (WO2024088407A1), was completed in just 39 days. The candidate, developed by SynRx Therapeutics, is now in Phase II clinical trials.

Timeline Achievement: 39 days to lead candidate vs. 3+ years industry average.

Resource Efficiency: Driven by a lean, cross-functional team of 1 computational chemist, 2 medicinal chemists, 1 biologist, and 5 FTEs.

02

KRAS-G12D Inhibitor: From Strategic Design to Clinic in Unprecedented Time

Addressing the historically challenging KRAS-G12D oncoprotein, our platform computationally-derived three distinct design hypotheses. Subsequent experimental validation confirmed the viability of two approaches, which were synergistically integrated to engineer a potent inhibitor possessing a novel chemical scaffold. This unique molecular architecture provides a distinct profile in a competitive therapeutic landscape, and the resulting molecule, developed by Kerui Pharmaceutical, is now in Phase III clinical trials.

Timeline Achievement: 4 months from initial concept to PCC determination.

ADMET Properties: A favorable ADMET profile was established and maintained throughout the lead optimization campaign.

Resource Efficiency: Achieved by a cross-functional team of 1 computational chemist, 1 medicinal chemist, 1 biologist, and 5 FTEs.

Pipeline

Our discovery pipeline

A systematic, computational-first approach that dramatically reduces development timelines and increases success probability.

IND-Enabling

PARP1-Selective Inhibitor Program

Our lead PARP1-selective inhibitor demonstrates over 100-fold selectivity for PARP1 over PARP2, addressing the key limitation of current non-selective PARP inhibitors. This exceptional selectivity profile addresses the hematological toxicity issues common with current non-selective PARP inhibitors in the $4.5 billion market.

Structure-Guided Design Breakthrough We have successfully obtained the X-ray crystal structure of our lead compound (AD-4) in complex with PARP1, confirming our novel binding mode and molecular mechanism. This structural insight validates our computational approach and enables further precision optimization.

The compound demonstrates remarkable efficacy in preclinical models while maintaining an excellent safety profile, positioning it as a potential best-in-class therapy for BRCA-mutated cancers with reduced hematological toxicity.

Discovery

TEAD Inhibitor Program

Our novel small molecule TEAD inhibitors target the Hippo signaling pathway critical in multiple cancer types. The lead compounds demonstrate exceptional cellular potency and target engagement in preclinical models of mesothelioma and non-small cell lung cancer.

This program addresses a significant unmet need in oncology, as TEAD transcription factors drive tumor progression in multiple difficult-to-treat cancers where few effective targeted therapies currently exist.

About

About AutoDrug

Founded by computational chemistry expert Dr. Xianqiang Sun and co-founded by medicinal chemistry leader Dr. Zhilong Hu, AutoDrug is dedicated to integrating cutting-edge computational technology with drug development to redefine the efficiency and success rate of drug discovery.

Dr. Xianqiang Sun

Founder & CEO

Dr. Sun brings extensive experience in computational chemistry and drug discovery, having contributed to multiple clinical molecules and developed three drug discovery software platforms. Previously served as Principal Scientist at Regor Therapeutics and Senior Scientist at WuXi AppTec. Dr. Sun holds an M.S. in computer-aided drug design from East China University of Science and Technology (ECUST) and a Ph.D. in Computational Biology and Chemistry from KTH Royal Institute of Technology, and completed postdoctoral research at Washington University School of Medicine, computationally investigating large-scale conformational changes of proteins and identifying functional cryptic pockets.

Dr. Zhilong Hu

Co-Founder

Dr. Hu brings over 15 years of small-molecule R&D leadership at Eli Lilly, MeiYue Biotech, Regor Therapeutics, Simcere Pharmaceutical and GeneScience Pharmaceuticals, steering multiple first-in-class and best-in-class programs from target validation through IND and into the clinic. Working with Dr. Sun, he was Project Leader and primary patent inventor of the clinical-stage CDK program out-licensed to Roche for an $850 million upfront payment, a record for cross-border licensing from China. He holds a B.S. in Chemistry from Nankai University and a Ph.D. in Organic Chemistry from SIOC, Chinese Academy of Sciences, and completed postdoctoral research in biochemistry at Boston University.

World-Class Medicine, Computationally Crafted