We explicitly strive to accelerate three areas of drug discovery and development:
Disease Target identification, Generation of Novel molecules data,
and Predicting Clinical trial outcomes

Our revolutionary drug discovery engine utilizes millions of data samples and multiple data types to discover signatures
of diseases and identify the most promising targets for billions of molecules that already exist or can be generated de novo with preferred sets of parameters
Enabling multi-omics target discovery and deep biology analysis engine to considerably reduce required time from several months to the span of just a few clicks
Find novel lead-like molecules in a week through this automated, machine learning de-novo drug design and scalable engineering platform
Predict clinical trials success rate, recognize the weak points in trial design, while adopting the best practices in the industry
Discover and Prioritize
Design and predict
Novel Targets
Novel Molecules
Clinical Trials
Access the full set of OMICs data generated by the scientific community so far. You do not need to spend your time trying to convert your data into an interpretable format or wait for a bioinformatician to do that for you — instead, you will find all the data already processed and uploaded in a uniform way, so you can focus on science and data interpretation.
Pathway analysis is a crucial step toward a complete understanding of how data works. It converts a list of seemingly unrelated genes into a connected story based on dysregulated molecular processes. PandaOmics uses a proprietary pathway analysis approach called iPanda to infer pathway activation or inhibition. Results published in Nature communications in 2016 demonstrated the algorithm outperforming other pathway analysis tools.
Proof of concept
in Nature Communications
PandaOmics scoring approach is based on the combination of multiple scores derived from text and omics data associating genes with a disease of interest. This kind of approach allows us to unveil the hidden hypotheses that might not be obvious over common general knowledge or simple bioinformatics analysis. AI tools are extremely helpful for efficient target hypothesis generation. The overall scoring approach results in the ranked list of target hypothesis for a given disease (or disease subtype).
Proof of concept
in Nature Communications
Proof of concept
in ACS Publications
Once the gene list is prioritized according to the required strategy. Druggability filter uses traffic light logic and allows to filter target hypotheses according to their novelty, accessibility by small molecules and biologics and safety. Safety accounts for the involvement of the genes in toxicity related pathways and lethality of the corresponding gene knockout in mice. Additional filters include the protein and mRNA expression tissue specificity and protein class
Define rewarding and penalty rules for molecule shape, chemical complexity, synthetic accessibility, metabolic stability, and other properties the novel molecules must satisfy.
Every new compound generated is annotated
with all the properties, including physico-chemical parameters, binding scores, drug-likeness features and mapped on vendors' catalogs and proprietary libraries for any similarity and novelty
1 May, 2023
Students use AI technology to find new brain tumor therapy targets — with a goal of fighting disease faster
30 April, 2023
New AI 'can accurately spot cancer': Algorithm is better at identifying cancerous nodules than existing methods, study claims
24 April, 2023
Drug discovery companies are customizing ChatGPT: here's how
17 April, 2023
How artificial intelligence is matching drugs to patients
13 April, 2023
Using AlphaFold, Insilico Medicine produces AI drug discovery in record time
13 April, 2023
Simplifying Target Discovery with an Artificial Intelligence Engine and Natural Language Chat
13 April, 2023
Insilico Medicine discovers small molecule CDK8 inhibitor to treat cancer
4 April, 2023
Disease and Age-Associated Targets Predicted Using AI Discovery Engine
1 April, 2023
The Key to a Precision Medicine Future: AI Plus Human Ingenuity
21 March, 2023
NVIDIA Unveils Large Language Models and Generative AI Service to Advance Life Sciences R&D
20 March, 2023
AI develops cancer treatment in 30 days, predicts survival rate
13 March, 2023
AI 'used to design entirely new cancer drug within just 30 days'
28 February, 2023
How an AI breakthrough from Alphabet's DeepMind 'changed scientific history' and could lead to new cures
28 February, 2023
Canadian-founded company develops first AI-designed COVID-19 drug, starts clinical trials
24 February, 2023
'It's perfect': World's first generative AI-designed COVID drug to start clinical trials
22 February, 2023
Accelerating Efficient Drug Discovery With the Power of AI
10 February, 2023
Insilico Gains FDA's First Orphan Drug Designation for AI Candidate
7 February, 2023
AlphaFold works with other AI tools to go from target to hit molecule in 30 days
6 February, 2023
Encoding creativity in drug discovery
2 February, 2023
Chemistry42: An AI-Driven Platform for Molecular Design and Optimization
15 October, 2020
Insilico partners with Taisho on end-to-end AI-powered senolytic drug discovery
27 April, 2020
Insilico and Astellas collaborate on AI for a conventionally challenging target family
14 April, 2020
Insilico collaborates with Boehringer Ingelheim on AI system for target discovery
16 March, 2020
Beijing Tide Pharmaceutical and Insilico Medicine enter into an agreement for Cancer Therapy
14 January, 2020
Insilico collaborates with Pfizer on novel data and AI system for target discovery