Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.

Area of Science:

• * Intersection of artificial intelligence and organic chemistry.
• * Synergistic advancements in computational chemistry and machine learning.

Background:

• * Organic chemistry presents unique data challenges: sparsity, heterogeneity, and combinatorial complexity.
• * These challenges have historically spurred innovation in artificial intelligence methodologies.

Purpose of the Study:

• * To review the bidirectional relationship between artificial intelligence and organic chemistry.
• * To highlight how chemical problems have catalyzed AI development.
• * To examine AI's impact on core areas of organic chemistry research.

Main Methods:

• * Survey of multimodal chemical data and molecular representations (fingerprints to geometric encodings).
• * Analysis of AI techniques including self-supervised learning, few-shot learning, hypergraph architectures, chemical language models, and autonomous agents.
• * Examination of advances in transfer learning, self-supervised pretraining, and meta-learning for molecular and reaction data.

Main Results:

• * Chemical data challenges have driven AI paradigms like self-supervised learning and hypergraph networks.
• * AI techniques are advancing reaction prediction, mechanistic inference, and retrosynthesis planning.
• * Generative molecular design and self-driving laboratories are emerging applications.

Conclusions:

• * The interplay between AI and organic chemistry is profoundly reshaping both fields.
• * Persistent challenges include data scarcity, bias, and reproducibility.
• * Future directions involve multimodal fusion, improved molecular representations, and bridging computational predictions with laboratory validation.