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Insilico Medicine

Founded Year



Grant | Alive

Total Raised




Last Raised

$700K | 6 mos ago



About Insilico Medicine

Insilico Medicine is an artificial intelligence-driven pharma-technology company. It provides artificial intelligence solutions for drug discovery, research, and development. The company delivers a range of generative adversarial networks (GANs) and reinforcement learning approaches to identify protein targets, generate molecular structures, and generate synthetic data. It was founded in 2014 and is based in Hong Kong, Hong Kong.

Headquarters Location

Unit 310, 3/F, Building 8W, Phase 2 Hong Kong Science Park, Pak Shek Kok, New Territories

Hong Kong,

Hong Kong


ESPs containing Insilico Medicine

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

Healthcare & Life Sciences / Drug R&D Tech

The generative AI — protein & drug design market offers solutions to the challenges faced by traditional drug development processes, which are inefficient and high-risk. By using generative AI, companies can significantly reduce the time and cost of drug discovery, accelerate the development cycle to design next-generation therapeutics and vaccines, and engineer proteins with fewer experiments. Th…

Insilico Medicine named as Leader among 11 other companies, including Atomwise, Cradle, and Cyclica.

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Expert Collections containing Insilico Medicine

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Insilico Medicine is included in 8 Expert Collections, including AI 100.


AI 100

199 items


Artificial Intelligence

10,924 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.


Biopharma Tech

6,079 items

Companies involved in the research, development, and commercialization of chemically- or biologically-derived therapeutic & theranostic drugs. Excludes vitamins/supplements, CROs/clinical trial services.


Digital Health 150

150 items

The winners of the second annual CB Insights Digital Health 150.



1,267 items

Companies involved in the capture, sequencing, and/or analysis of genomic, transcriptomic, proteomic, and/or metabolomic data


Digital Health

10,338 items

The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.

Insilico Medicine Patents

Insilico Medicine has filed 38 patents.

The 3 most popular patent topics include:

  • Transcription factors
  • Clusters of differentiation
  • Artificial neural networks
patents chart

Application Date

Grant Date


Related Topics




Artificial neural networks, Machine learning, Classification algorithms, Machine learning algorithms, Computational neuroscience


Application Date


Grant Date



Related Topics

Artificial neural networks, Machine learning, Classification algorithms, Machine learning algorithms, Computational neuroscience



Latest Insilico Medicine News

Insilico Medicine Sees Potential Quantum Advantage in Using Quantum Generative Adversarial Networks in Generative Chemistry

May 22, 2023

Insider Brief Insilico Medicine used quantum computing and generative AI to explore the lead candidate discovery in the drug development process. The team added that they demonstrated the potential advantages of quantum generative adversarial networks in generative chemistry. Critical Quote: “The application of quantum computing in drug discovery will potentially help reduce the time and lower the cost of research and development.” — Min-Hsiu Hsieh, PhD, Director of the Quantum Computing Research Center of Hon Hai Technology Group PRESS RELEASE — Insilico Medicine, a clinical stage generative artificial intelligence (AI)-driven drug discovery company, today announced that it combined two rapidly developing technologies, quantum computing and generative AI to explore the lead candidate discovery in the drug development process and successfully demonstrated the potential advantages of quantum generative adversarial networks in generative chemistry. The study published on May 13th in the American Chemical Society’s  Journal of Chemical Information and Modeling , a leading journal in computational modeling, is led by Insilico Taiwan center and UAE center which focus on pioneering and constructing breakthrough methods and engines with rapidly developing technologies including generative AI and quantum computing to accelerate drug discovery and development, supported by the University of Toronto’s Acceleration Consortium director Alan Aspuru-Guzik and Hon Hai (Foxconn) Research Institute. “We are pleased to achieve the milestone in the collaboration with Insilico Medicine. Quantum computing can be used to solve complex computational problems. The application of quantum computing in drug discovery will potentially help reduce the time and lower the cost of research and development,” said  Min-Hsiu Hsieh , PhD, Director of the Quantum Computing Research Center of Hon Hai Technology Group (Foxconn®) Generative Adversarial Networks (GANs) are one of the most successful generative models in drug discovery and design which has shown remarkable results for generating data that mimics a data distribution in different tasks. The classic GAN model consists of a generator and a discriminator. The generator takes random noises as input and tries to imitate the data distribution, and the discriminator tries to distinguish between the fake and real samples. A GAN is trained until the discriminator cannot distinguish the generated data from the real data. In this paper, researchers have explored the quantum advantage in small molecule drug discovery by substituting each part of MolGAN, an implicit GAN for small molecular graphs, with a variational quantum circuit (VQC) step by step including as the noise generator, generator with the patch method and quantum discriminator, and comparing its performance and with the classical counterpart. The study not only demonstrates that the trained quantum GANs can generate training-set-like molecules by using the VQC as the noise generator, but the quantum generator outperforms the classical GAN in the drug properties of generated compounds and the goal-directed benchmark. In addition, the study shows the quantum discriminator of GAN with only tens of learnable parameters can generate valid molecules and it outperforms the classical counterpart with tens of thousands of parameters in terms of generated molecule properties and KL-divergence score. “Quantum computing is recognized as the next technology breakthrough which will make great impact to all communities, and the pharmaceutical industry is believed to be among the first wave of industries benefiting from the advancement. The paper demonstrates Insilico’s first footprint in quantum computing with AI in molecular generation underlining our vision in the field,” said Jimmy Yen-Chu Lin, PhD, GM of Insilico Medicine Taiwan, and corresponding author of the paper. The promising result will further support Insilico’s UAE team to integrate the hybrid Quantum GAN model into Chemistry42, Insilico’s proprietary small molecule generation engine to obtain more efficient and accurate results in AI-driven drug discovery and development process. As one of the pioneers to leverage GANs in de novo molecular design Insilico  published the first paper in this field in 2016  and the company has delivered 11 preclinical candidates with the support of its end-to-end platform Pharma.AI based on generative AI models since 2021, three of which have entered clinical trials. “To our knowledge, it is the first time in the industry to systematically replace every component of GAN with VCQ and successfully generate molecules. I believe this is also the first small step in our journey,” said Alex Zhavoronkov, founder and CEO of Insilico Medicine. “We are committed to accelerate high-quality effective therapeutics to patients to extend healthy productive life for everyone on the planet with the support of cutting-edge technologies. Insilico’s UAE center is currently working on a breakthrough experiment with a real quantum computer for chemistry and look forward to sharing Insilico’s best practices with industry and academia.”

Insilico Medicine Frequently Asked Questions (FAQ)

  • When was Insilico Medicine founded?

    Insilico Medicine was founded in 2014.

  • Where is Insilico Medicine's headquarters?

    Insilico Medicine's headquarters is located at Unit 310, 3/F, Building 8W, Phase 2, Hong Kong.

  • What is Insilico Medicine's latest funding round?

    Insilico Medicine's latest funding round is Grant.

  • How much did Insilico Medicine raise?

    Insilico Medicine raised a total of $402M.

  • Who are the investors of Insilico Medicine?

    Investors of Insilico Medicine include Bill & Melinda Gates Foundation, Pavilion Capital, BOLD Capital Partners, Qiming Venture Partners, Warburg Pincus and 29 more.

  • Who are Insilico Medicine's competitors?

    Competitors of Insilico Medicine include Hexis Lab, Valence, InstaDeep, Pepper Bio, AceMap, Cradle, InSilicoTrials, VeriSIM Life, Endel, DEARGEN and 17 more.

Compare Insilico Medicine to Competitors


Cradle operates as a biotechnology company. It uses artificial intelligence (AI) assisted design tools to predict the 3D structure of a protein, generate new sequences with thermostability, optimize codons, and more. It serves biologists. The company was founded in 2021 and is based in Amsterdam, Netherlands.

Biomatter Designs Logo
Biomatter Designs

Biomatter Designs is pioneering the technologies for generative protein design at the intersection of synthetic biology and AI.

Variational AI Logo
Variational AI

Variational AI is a Vancouver, British Colombia-based company leveraging artificial intelligence to help develop new small molecule drugs.

Aqemia Logo

Aqemia offers drug discovery software predicting the affinity between drug candidates and therapeutic targets responsible for diseases. It uses quantum and statistical mechanics algorithms fuelling a generative artificial intelligence (AI) to design drug candidates. The company was founded in 2019 and is based in Paris, France.

YDS Pharmatech

YDS Pharmatech is a drug discovery platform-modified evaluate reinforce evolve sampling (MERES). MERES has various proprietary molecular modifiers, biophysical property calculators, and AI learning engines. YDS Pharmatech was founded in 2020 and is based in Albany, New York.

Atomwise Logo

Atomwise develops machine learning-based discovery engines and uses deep learning and artificial intelligence (AI)-based neural networks to help discover new medicines. It predicts drug candidates for pharmaceutical companies, start-ups, and research institutions and designs drugs using computational drug design. The company was founded in 2012 and is based in San Francisco, California.

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