World-Class Medicine, Computationally Crafted

AutoDrug (傲图智药) designs next-generation therapeutics using advanced computational tools that transform discovery timelines from years to weeks.

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Our Core Technology

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.

🎯AutoDock Engine

High-throughput molecular docking platform that evaluates millions of compound-target interactions with quantum-level accuracy. Our proprietary scoring functions predict binding affinity within 1 kcal/mol precision, dramatically reducing false positives in hit identification.

🧬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 Optimization

Leveraging quantum computing principles for molecular optimization, we explore vast chemical spaces impossible with classical methods. This enables discovery of novel binding modes and unexpected chemical solutions.

Our Services

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

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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.

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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.

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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.

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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

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

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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 lead candidate has since progressed into Phase I clinical trials, demonstrating the platform's capacity to dramatically compress discovery timelines.

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.

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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 has now entered Phase I 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.

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 AUTODRUG

Founded by computational chemistry expert Dr. Xianqiang Sun, 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, with a postdoctoral fellowship at Washington University School of Medicine.

Dr. Na Kong

Operations Director

Dr. Kong earned her Ph.D. from the Royal Institute of Technology in Sweden and serves as an Assistant Professor at Shanghai Tech University. Her research has been published in leading international journals including Science, bringing valuable academic rigor and operational excellence to our team.