Since the late 20th century, the exploration into drug discovery empowered by artificial intelligence has lasted for decades, revealing the promise of AI in complex data integration, de novo molecular design, and clinical translation. Yet, providing solid guidance on exactly how and where these technologies can be most effectively integrated remains a formidable challenge.
On January 9, 2026, the latest edition of Applied Artificial Intelligence for Drug Discovery was published online as a Springer Nature volume, spanning 27 chapters authored by leading international experts to present state-of-the-art approaches in the whole drug discovery process. As a pioneering global biotechnology company, Insilico Medicine (3696.HK) made exclusive contributions to two chapters in the comprehensive and forward-looking volume, sharing experience in real-life application of AI in early drug target-related tasks including evaluation, and giving prospects for the future assisted with quantum computing. The book has obtained 1700+ accesses, only 10 days after publication.
The “Artificial Intelligence for Drug Target and Pathway Identification, Assessment, Validation, and Indication Expansion” chapter was led by Alex Zhavoronkov PhD, founder and CEO, CBO of Insilico Medicine, demonstrating the AI-driven biology workflow from data analysis, target prediction and pathway identification, all the way to target validation and assessment, and finally indication exploration. The chapter also looks into future key challenges including data quality and regulatory changes.
Since the landmark paper in Nature Communications introducing iPANDA (In Silico Pathway Activation Network Decomposition Analysis) in 2016, Insilico Medicine has been standing at the forefront of AI-driven drug discovery. In 2019, Insilico published in Nature Biotechnology about how its self-developed deep generative model GENTRL discovered a potent inhibitor for the kinase target DDR1 in just 21 days, and the models eventually gave rise to Pharma.AI, the comprehensive generative AI platform across biology, chemistry, clinical development and science research. In 2025, Insilico further approaches the future of Pharmaceutical Superintelligence (PSI) through the publication of Target Identification Pro (TargetPro) with a 71.6% retrieval of known clinical targets, TargetBench 1.0, the first standardized benchmarking framework of its kind, and LEGION (Latent Enumeration, Generation, Integration, Optimization and Navigation) the AI workflow to cover patent holes in chemical exploration.
On the even more transformative side is the “Drug Discovery with Quantum Machine Learning” chapter led by Alex Aliper PhD, Co-founder and President of Insilico Medicine, where the roadmap to 2030 starting with Quantum Machine Learning (QML) algorithms is presented, with several case studies of successful applications in drug discovery.
In 2023, Insilico first demonstrated the potential of Quantum Generative Adversarial Networks in the Journal of Chemical Information and Modeling, showing the possibility to overperform traditional models with quantum computing. Starting from here, Insilico embarked on the journey of quantum-assisted innovation, announcing the novel algorithm QFASG (Quantum-assisted fragment-based automated structure generator) in 2024 on Frontiers in Chemistry, successfully identifying low micromolar inhibitors for ATM and CAMKK2 kinases; and the quantum computing-enhanced AI case study in 2025 on Nature Biotechnology, where novel inhibitors against KRAS the “undruggable” target is identified using a hybrid quantum-classical generative model.
“I am proud to see our decade of pioneering research serve as a cornerstone for this fundamental book for anyone interested in AI-driven drug discovery, as this completes the cycle from theoretical exploration to academic publication, then real-world application, and back to industry-level guide”, said Alex Zhavoronkov PhD, founder and CEO, CBO of Insilico Medicine. “We are providing the roadmap for a transition from traditional R&D to a state of Pharmaceutical Superintelligence (PSI), where AI agents actually make decisions to minimize failure in the clinic. Our mission is far more than just treating symptoms, it is about extending healthy longevity for everyone on the planet, and AI is accelerating that process.”
By integrating the technologies of AI and automation, Insilico has demonstrated significant efficiency boost compared to traditional drug discovery methods (often requiring an average of 4.5 years), as announced in the recent key timeline benchmarks for internal programs from 2021 to 2024: the average time to PCC is 12-18 months, with 60-200 molecules synthesized and tested per program.
About Insilico Medicine
Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend healthy longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.
By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma.AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine. For more information, please visit www.insilico.com