Dublin, July 20, 2022 (GLOBE NEWSWIRE) -- The "Growth Opportunities in AI for Drug Target Discovery and Validation" report has been added to ResearchAndMarkets.com's offering.
The score provided by the AI algorithms is responsible for the prioritization of the drug target. High-score targets are more likely to be considered for further experimental validation. The most extensively used deep learning algorithms are convolution neural networks (CNNs), recurrent neural networks, deep belief networks, and deep neural networks.
Pharmaceutical companies are beginning to understand the value of AI due to its increasing adoption across several industries. AI algorithms are being trained using advanced biology and chemistry data, making them more efficient in terms of making accurate decisions and identifying missing links.
AI in drug discovery is creating a new era of virtual drug discovery labs that are capable of both novel drug target identification and drug molecule discovery. Pharmaceutical companies are making significant investments in collaborative programs or licensing programs in AI platforms to identify novel therapeutic drugs for various diseases.
A major challenge in drug discovery is the limited number of gene targets that can be targeted for the development of a drug molecule. Only close to 400 genes are proven targets for US FDA-approved drugs. 80% of human genes are unexplored as targets, and some are considered undruggable.
The identification and the selection of novel drug targets (proteins/genes) have remained major challenges in drug discovery as the identification of a molecular target requires assessment of genomics, proteomics, and in vitro and in vivo experimental data interpretation. The challenge lies in the lack of technology to break down complex biological networks and map them thoroughly.
Scientists find it difficult to mine through large volumes of biological data and draw meaningful insight from them. The identification of novel drug targets will open up opportunities for the development of new drug molecules and the provision of treatment options for diseases with limited/no options.
The identification of novel drug targets is built on the hypothesis developed by researchers, which is based on scientific literature and available databases, followed by experimental data that supports the hypothesis. Experimental approaches for drug target identification involve a series of experiments and the assessment of the modulations caused at the drug target site.
Most hypotheses do not yield the desired outcomes and provide negative results. Carrying out in vitro studies and developing multiple assays for target identification are lengthy and time-consuming processes that involve high costs to set up experiments. Moreover, the manual interpretation of vast amounts of biological data is challenging and time consuming.
Over the years, significant advances have been made to identify novel drug targets using different approaches, including advanced bioinformatics tools and automated experimental designs, but these have failed to provide fruitful outcomes and led to financial burdens on drug developers. Nevertheless, researchers continue to develop platforms that aid successful novel target discovery.
Today, with the help of AI and ML algorithms, it is possible to identify new drug targets; moreover, developments in new modalities enable the targeting of undruggable targets, thereby increasing the success rate of drug discovery. An AI algorithm can predict a potential target through its scoring mechanism based on the data collected from gene expressions, protein-protein interactions, clinical trials, and disease biologies.
Key Topics Covered:
1. Strategic Imperatives
- The Strategic Imperative: Factors Creating Pressure on Growth
- The Impact of the Top 3 Strategic Imperatives on AI for Drug Target Discovery and Validation
- Growth Opportunities Fuel the Growth Pipeline Engine
- Research Methodology
2. Scope of Analysis
- Research Context
- Research Scope
- Key Findings
3. Growth Environment Analysis: Drug Target Discovery
- Increased Costs and Unpredictable Outcomes Limit Drug Target Discovery
- Deep Learning to Drive Novel Drug Target Identification
- AI Platforms Analyze Complex Biological Data from Different Sources to Arrive at Suitable Drug Targets
- Parameters Covered by AI that Result in the Selection of Novel Target Identification
- Enablers of AI-based Novel Drug Target Discovery
- Recent Developments Driving AI-based Drug Target Discovery
4. Target Discovery and Identification: Technology Analysis
- Advancements in Deep Learning are Enabling the Delivery of Valuable Outcomes
- Artificial Neural Networks are Being Used to Identify Novel Drug Targets
- Advances in Phenotype Screening for Target Discovery Using AI
5. Application Landscape
- Disease Focus Areas for AI-enabled Drug Discovery
- Oncology
- Neurodegenerative Diseases
- Respiratory Diseases and Cardiovascular Diseases
- Autoimmune Diseases and Rare Diseases
6. Funding and Commercial Landscape
- The Private Sector Funding Landscape
- The Investment Portfolio of Companies Performing AI Target Identification
- The Investment Stages of Companies Performing AI Target Identification
- Funding Landscape - Key Takeaways
- Strategic Pharmaceutical and AI Drug Discovery Company Collaborations for Target Identification
- A Highly Collaborative Network of AI and Pharmaceutical Companies Involved in Target Identification
7. Growth Opportunities
- Growth Opportunity: Intuitive AI Platforms
- Growth Opportunity: Training Algorithms on Unbiased Data
- Growth Opportunity: AI drug discovery companies to focus on internal pipelines
8. Appendix
- Simplified Workflows Using AI to Encourage Customer Acquisition for Drug Target Identification
- Current Challenges in Adopting AI-based Novel Drug Target Identification
- The Emergence of -Omics Databases is Enabling ML Algorithms to Identify Targets
- AI Platforms are Leveraging -Omics Data for Target Discovery
9. Next Steps
For more information about this report visit https://www.researchandmarkets.com/r/mhew08
