La revista Chemistry World ha publicat l’entrada Twenty ways AI is advancing chemistry on s’hi analitza com la IA està modulant la química actual, basant-se en l’article de Valentine P. Ananikov publicat a Artificial Intelligence Chemistry Top 20 influential AI-based technologies in chemistry.
Anakinov esmenta aquests punts (per cert, ajudat en la redacció pel ChatGPT-4):
- AI-driven Drug Discovery: AI can be used to accelerate the identification of potential drug candidates through the prediction of biological activity and optimization of lead compounds.
- Big Data and Integrated Data: Consolidation and harmonization of diverse chemical data sources, facilitating cross-disciplinary research and comprehensive data analysis.
- Automated Laboratory Platforms: Development of automated laboratory systems for conducting experiments with minimal human intervention, improving precision and reproducibility.
- Integration of Laboratory Instruments and IoT: Laboratory instrument connectivity to the Internet of Things (IoT) for real-time data collection, monitoring, and analysis.
- AI in Spectroscopy and Analytical Method Development: AI applications improving complex spectroscopic data interpretation and developing novel analytical methods.
- Blockchain in Chemical Supply Chain: Blockchain technology for secure, transparent chemical and material supply chain tracking.
- Digital Twins: Creation of virtual replicas of chemical processes or systems for simulation, monitoring, and optimization purposes.
- Virtual Laboratories and Augmented Reality: Digital platforms and simulation software enhancing teaching and providing virtual lab experiences.
- Natural Language Processing (NLP) in Chemical Space: NLP tools for mining chemical information from scientific literature, patents, and databases for knowledge extraction.
- Predictive Toxicology: Computational models predicting chemical toxicity to enhance environment protection and chemical manufacturing safety.
- AI in Environmental Chemistry and Sustainability: Digital tools aiding environmental process analysis, pollution control, and green chemistry development.
- Machine Learning in Molecular Design: Application of ML algorithms to predict molecular properties, enabling efficient design of new compounds and materials.
- Smart Control: Implementation of intelligent control systems in chemical processes and equipment, enabling adaptive and optimized operations.
- Deep Learning in Structure-Activity Relationships (SAR): The utilization of deep learning models to decipher and predict the complex relationships between chemical structures and their biological activities enhances the efficiency of drug discovery processes.
- AI-driven High-throughput Experimentation (HTE): Robotics and AI integration for conducting and analyzing multiple parallel experiments, accelerating the research process.
- Digital Materials Design and Materials Informatics: Data-driven approaches for discovering and designing new materials with desired properties and applications.
- Data-Driven Chemical Reaction Optimization: Machine intelligence models predicting reaction outcomes, optimizing conditions, and discovering new reactivity.
- Automated Synthesis Planning: Assistance from AI tools in planning and optimizing synthetic routes, reducing experimental trial time and resources.
- Chemoinformatics and Chemical Data Analysis: Advanced techniques for managing and interpreting large chemical datasets, enhancing understanding of complex relationships in chemical structures and processes.
- AI in Quantum Chemistry and Simulations: AI-enhanced accuracy and efficiency in quantum chemical calculations and molecular simulations.
Els punts que més afecten el meu entorn de química teòrica i computacional són sobretot el 20 i el 12, però igualment el 16, també l’1 i 14, i potser també el 9 de processament de llenguatge natural. Un dels objectius que tinc d’ara endavant és poder entendre una mica el punt 20 i el 12 (que ja entenc una mica).
De fet, gairebé tots aquests punts, potser amb l’excepció del 9, estan més vinculats al machine learning, i en canvi no gaire a la IA generativa, la que més origina debat, reflexió i crítica actualment.
El Chemistry World ho comenta així:
AI is also reshaping the chemical industry in profound ways, with analysts predicting that its impact will surpass even that of the internet. From algorithms that accelerate molecular design to automated labs that enhance the speed and accuracy of experiments, AI is transforming how chemists solve complex problems. Its impact extends across materials discovery, reaction optimisation and sustainability efforts, making it an indispensable force in modern chemistry.
Un assumpte apassionant, doncs.