The Commoditization of AI for Molecule Design.

Area of Science:

• Computational chemistry
• Drug discovery
• Materials science

Background:

• The COVID-19 pandemic accelerated the adoption of computational technologies in molecule design.
• Artificial intelligence (AI) and machine learning (ML) have become integral tools for scientists working remotely.
• AI and ML are transforming the pharmaceutical industry, becoming a commodity for molecule design and optimization.

Purpose of the Study:

• To provide an opinion on the evolution and application of ML in modeling molecular properties across industries.
• To explore the integration of AI into automated experimental pipelines and equipment.
• To highlight the impact of AI and ML on molecule design and the drug discovery process.

Main Methods:

• Review of current AI and ML applications in molecule design.
• Discussion of generative models and their architectures for *de novo* molecule design.
• Analysis of industry trends and companies leading AI adoption in molecule design.

Main Results:

• AI and ML are increasingly used for designing and optimizing molecules.
• Generative models are being implemented for *de novo* molecular design.
• AI is influencing and impacting molecule design workflows across various industries.

Conclusions:

• AI and ML are poised to significantly increase the efficiency of the design-make-test cycle.
• The future of molecule design will involve tighter integration of AI into automated experimental pipelines.
• Continued advancements in AI technologies will shape the future of molecular discovery and development.