The global in-silico drug discovery market size was valued at USD 2.38 billion in 2021 and is projected to reach USD 5.86 billion by 2030 at a CAGR of 10.45% from 2022 to 2030.
In silico, also known as computer-aided drug design, is a method in which a drug is designed or discovered using computational techniques. New drug molecules that can bind to a specific target are identified through the drug discovery process. This technique for identifying drugs is based on computation, and the entire procedure is regarded as linear and sequential. Several factors make the in-silico drug discovery method more effective and prudent than earlier drug discovery methods employed in the drug development process. In-silico drug design can play a vital role in all phases of drug development, from the initial lead design to the final clinical stage.
The in-silico drug discovery industry is driven by the increasing use of cloud applications. Cloud computing provides access to virtually limitless computational resources, enabling pharmaceutical researchers to scale their computing environment as necessary. Organizations can more easily tailor solutions to their unique needs. BT's Cloud Compute life sciences platform, for instance, can provide specific pharmaceutical applications across the value chain, from discovery to commercial operations, ensuring that the required applications work across pipelines, as opposed to forcing all pipeline teams to use the same application.
These applications increase overall productivity and reduce the likelihood of clinical failure by enabling drug developers to analyze historical data as part of their respective clinical research activities.
In 2021, artificial intelligence held the largest market share, exceeding 40%. As the rate of data digitization in the healthcare industry increases, AI is being utilized to address challenges such as acquiring, analysing, and applying knowledge to solve complex clinical problems. AI imitates human intelligence and is able to differentiate between hit-and-lead compounds, allowing for faster validation of therapeutic targets and structural design optimization. Consequently, it is utilized effectively in various phases of drug discovery, such as drug design, chemical synthesis, drug screening, polypharmacology, and drug repurposing.
Rapid technological advancements in the field of computational biology are also propelling the global in-silico drug discovery industry. The development of new pharmaceutical molecules has been aided by computational methods. The data processing and analysis phases of sequencing have been further simplified by advances in computational biology, resulting in a reduction in turnaround time and an increase in precision.
Industry-wide demand for more integrated storage and computational node utilisation strategies has resulted from these advancements. This integration is anticipated to reduce data transfer costs and eliminate bottlenecks in downstream analysis and communication within the computational analytics community. Virtual screening is a common computational technique for identifying hits in the earliest phases of drug development.
The one and only restraint of the In-Silico Drug Discovery market is its limited time period. This method is computationally intensive and dependent on the size of the stimulated systems, with analysis times ranging from tens to hundreds of nanoseconds. The issue with this is that the time period, which can range from milliseconds to seconds, is frequently too short to determine protein folding. Consequently, this can result in an insufficient sampling of protein conformations.
In the in-silico drug discovery market, SaaS is expected to experience the highest CAGR, around 11%, over the forecast period. This will be primarily due to the fact that these solutions aid users in data mining, data analysis, and decision making by providing decentralization, real-world data management, and numerous other features. In addition, these solutions aid in reducing the total cost and duration of the drug discovery procedure. In addition, they are increasingly utilised in the drug discovery process for virtual screening and target fishing.
The global In-Silico Drug Discovery market can be segmented on the basis of product, technology, workflow, end-users and geographical regions. Artificial Intelligence is the most dominant technology in the market and Software-as-a-service is the most dominant product type of the In-Silico Drug Discovery market.
Based on the target therapeutic area, the in-silico drug discovery market is divided into HIV, infectious diseases, metabolic disorders, mental disorders, musculoskeletal disorders, neurological disorders, oncological disorders, respiratory disorders, skin disorders, urogenital disorders, autoimmune disorders, blood disorders, cardiovascular disorders, gastrointestinal and digestive disorders, hormonal disorders, and others.
The global market for in-silico drug discovery can be segmented by region into North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. During the forecast period, it is anticipated that North America will dominate the global in-silico drug discovery market. During the forecast period, the in-silico drug discovery market in the region is anticipated to be driven by technological advancements, the presence of strong vendors, a large patient population suffering from various chronic and infectious diseases, such as COVID-19 and chronic kidney disease, and a growing government emphasis on improving healthcare infrastructure.
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