Drug discovery is a complex process that spans from identifying potential compounds to obtaining regulatory approval. Trial-and-error techniques in medicinal chemistry have given way to more comprehensive methodologies due to advances in computational chemistry and biological data analysis. Drug development has been transformed by computational methods, especially artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), which improve data analysis and predictive modeling. AI is becoming integral to the development of botanical drugs. It is being used to identify potential novel medications derived from plant compounds and to improve the selection of therapeutic targets.
The report includes:
- Analysis of the impact of artificial intelligence on global botanical and plant-derived drugs market
- Case studies for successful integration of AI in botanical and plant-derived drugs market
- The analyst's viewpoint on how AI and automation is redefining the market landscape for botanical and plant-derived drugs
- Highlights of AI adoption trends across the major geographical regions as well as disruptions caused by AI across key industry verticals
- Review of AI impact on the various stages of the global supply chain
- Competitive intelligence featuring key strategic initiatives, market spendings and an investment outlook
Table of Contents
Chapter 1 Key Use Cases for AI Implementation- Overview
- Key Use Cases
- Use Case 1: Drug Discovery
- Use Case 2: Drug Discovery
- Overview
- Adoption Trends by Region
- Adoption Trends in North America
- Adoption Trends in Europe
- Adoption Trends in the Rest of the World
- Overview
- Impact of AI on Key Industry Verticals
- Impact of AI on Various Stages of the Value Chain
- Market Spending and Forecasting Analysis
- Abbreviations Used in the Report
Table 1: Abbreviations Used in AI Impact on Botanical and Plant-Derived Drugs Market
List of Figures
Figure 1: Botanical Drugs Value Chain
