In Silico Drug Discovery Market: Accelerating Drug Development

In Silico Drug Discovery Market is using AI to cut costs and speed up R&D.

The term "in silico" describes computational models used to explore pharmacological hypotheses through various techniques, including databases, data analysis tools, data mining, homology models, machine learning, pharmacophores, quantitative structure-activity relationships, and network analysis tools.

The Global In-Silico Drug Discovery Market is projected to grow from $3.6 billion in 2024 to $6.8 billion by 2030, registering a CAGR of 11.2% during the forecast period.

In silico drug design utilizes computational techniques and models to identify drug-like molecules through bioinformatics tools. These methods help analyze and predict the biological activity of potential drug candidates while also assessing their physicochemical properties.

Traditional Drug Discovery V/S In-Silico Drug Discovery

Conventional drug design approaches require significant time and effort. In silico methods play a vital role in drug discovery and clinical research, offering cost efficiency, ethical advantages, and accelerated processes. As technology advances, these approaches are poised to become essential, fostering innovation, personalized medicine, and new breakthroughs in the biomedical field.

In 2024, the In-Silico Drug Discovery Market was valued at USD 3.6 billion, and it is projected to reach USD 6.8 billion by 2030, reflecting a robust compound annual growth rate (CAGR) of 11.20% during the forecast period. This surge is underpinned by the rising adoption of AI-led platforms, increasing pharmaceutical R&D digitization, and the shift toward in-silico-driven preclinical testing.

According to industry, almost 35% of the total cost and time invested in developing a new drug can be saved by adopting an in-silico approach.

Role of Gen AI in Drug Discovery

Artificial Intelligence (AI) is increasingly at the forefront of drug discovery, especially in the screening and hit identification phase, where it enables faster, cost-efficient, and high-accuracy analysis of large chemical libraries. The AI-powered screening segment has emerged as one of the fastest-growing pillars of the in-silico drug discovery landscape.

Major Drug Candidates in Pipeline (2024–2025)

Key Trends and Adoption Insights:

• AI screening technologies are helping reduce early-stage R&D timelines by 6 to 9 months, with estimated 40% reductions in early-stage failure rates in projects adopting AI for lead prioritization.

• Over 65% of top 50 pharmaceutical companies have implemented AI tools for target screening and hit triaging, either through in-house platforms or partnerships with AI-native biotechs.

• More than 500 companies globally are actively working on AI screening platforms, with significant traction in the U.S., Europe, and select hubs in Asia-Pacific (Japan, South Korea, Singapore).

Emerging Platforms and Initiatives

Strategic Partnerships & Collaborations

• Evotec SE and Bristol Myers Squibb: In April 2025, Evotec announced significant progress in its collaboration with Bristol Myers Squibb, focusing on molecular glue-based drug discovery. This partnership, initiated in 2018 and expanded in 2022, combines Evotec's PanOmics and AI-supported platforms with Bristol Myers Squibb's CELMoD™ library. The collaboration has yielded a growing pipeline of molecular degraders targeting high-value oncology and other indications, resulting in $75 million in milestone payments to Evotec.

• Novo Nordisk and Valo Health: In January 2025, Novo Nordisk expanded its agreement with Valo Health to develop treatments for obesity, type 2 diabetes, and cardiovascular diseases. The extended deal includes near-term payments up to $190 million and potential milestone payments totaling $1.9 billion for nine new drug programs. The collaboration leverages Valo's AI-driven, human-centric drug discovery platform.

• GSK and Relation Therapeutics: In December 2024, GSK entered a $300 million partnership with UK-based Relation Therapeutics to develop treatments for osteoarthritis and fibrotic diseases. Relation applies machine learning to generate data from human tissue, aiming to reduce drug discovery costs and failures. The deal includes a $45 million initial payment, with potential success-based payments up to $263 million per drug target.

Convergence Trends Redefining the Market

• From Workflow Silos to Integrated AI Pipelines: Early adopters like Recursion, Exscientia, and Insilico Medicine are collapsing the traditional walls between target ID, hit generation, and lead optimization through AI/automation fusion.

• From Point Solutions to End-to-End Stack Providers: Big pharma increasingly favors partners who can handle multi-omics integration, simulation, and AI-guided candidate selection under one roof.

• From Experimental First to Hypothesis-First R&D: In-silico predictions are now dictating which biology to test, not just how to optimize it—flipping the traditional paradigm and accelerating decisions at preclinical stages.