At first glance, chemistry and artificial intelligence (AI) may seem like two different things, but beyond this, they are more and more merging into the modern technology development. The problem is complex, but that solves AI’s problem; as computational power and big data analysis methods become increasingly sophisticated, a tool as inspired as AI is uniquely well-suited to solving chemical problems. At the same time, new AI is being approached with chemical insights. These fields are complementary, and their intersection is very likely to produce many of the technologies of the future.
This article will discuss the many ways chemistry and AI are converging and why this is beneficial from the perspective of technology. We will present some promising research directions and real-world applications related to materials science, drug discovery, alternative energy and manufacturing. In addition, we will also discuss some of the challenges to come and outlooks from leaders working in these spaces.
AI Assisting Chemistry
Historically, making advances in chemistry has been heavily reliant on physical experimentation and human intuition. However, chemical datasets are often incredibly complex, with huge combinatorial spaces of possible materials and compounds. Manually discerning insights from these datasets is enormously difficult. AI methods, like machine learning and deep learning, are therefore becoming vital to identifying patterns, predicting properties, directing experiments, and optimizing chemical systems. For example, tools such as the Smodin chemistry AI solver help automate problem-solving by providing instant solutions to complex chemical equations.
In materials science, for instance, high-throughput computational screening of thousands of candidate compounds is now achievable with AI. Where previously less than 1% of known inorganic materials had been characterized; data-driven AI models can now predict key material properties at large scale based on composition alone.
This widens the range of possible materials to use in electronics, the energy sector, construction and more. AI can also help in predicting reaction yields, optimizing chemical reaction conditions, as well as even suggesting entirely new reaction pathways. AI automates the early discovery stage to allow the researchers to concentrate on the most promising material and reaction candidates without spending the resources.
AI is also making progress in drug development by using computers to look at molecular structures, model how targets bind and interact, and guess what biological activities might happen. Where wet lab drug screening experiments might test thousands of molecular candidates, AI techniques allow screening of billions of candidates in silico. This expands possibilities and improves odds in the perennially costly and time-intensive hunt for new drug compounds. AI holds particular promise for fast-tracking drug repurposing - identifying new uses for existing approved drugs.
Across chemical domains, AI’s ability to rapidly infer insights from massive, multifaceted chemical datasets is allowing innovations not otherwise possible. While the tools in AI are powerful, there is still a role for human chemical intuition and guidance in helping contextualize results and create the most productive experiments. Humans and AI are taking the chemical progress line further at a rate greater than either could alone.
Chemistry Inspiring Advances in AI
Tools that AI offers to speed up chemical discovery are also inspiring all new AI techniques inspired by chemistry. An important class of graphical representations of molecular structures whose properties and interactions can be inferred is being used by chemists. These intuitive visual reasonings are being translated into graph-based neural networks. Molecular graphs are proving uniquely effective for learning molecular features relevant to drug activity, material behaviors, and more.
Other chemical concepts like retrosynthesis – breaking down target molecules into simpler precursor compounds – are also serving as templates for new AI approaches to modular, hierarchical reasoning. Just as chemists logically decompose synthesis plans, retrosynthesis AI breaks down complex problems into layers of simpler inference steps. Hierarchical and modular reasoning has shown promise for multistep decision-making in robotics, natural language processing, and other complex domains.
Chemists additionally have long experience intelligently navigating uncertainty, noisy data, and risk-reward tradeoffs. Statistical methods and heuristics from chemistry research are now informing AI techniques for exploration, experiment design, and optimization in situations with imperfect information. These methods help AI systems dynamically adapt experiments based on incoming data, while still systematically probing uncertainty over time.
Automated lab science is directly benefiting from AI agents that learn more about chemicals over time by doing mix-and-match combinatorial experiments. Such hybrid human-AI exploration will be increasingly critical where labeled training data is scarce, but environmental interaction is possible.
Automated lab science is directly benefiting from AI agents that learn more about chemicals over time by doing mix-and-match combinatorial experiments providing inspiration for AI advances. Chemical insights offer intuitively understandable templates for tackling multifaceted, hierarchical real-world problems with uncertainty. This interdisciplinary cross-pollination between chemistry and AI is vital for developing more robust, trustworthy AI systems.
Real-World Applications
The fusion of chemical and artificial intelligence is enabling breakthrough applications across industries:
Sustainable Manufacturing
Minimizing waste and emissions (and consequently profit) from large, chemical production, is the goal of AI process modeling and optimization. Dynamic predictions designed to predict machine degradation and micro defects in material are also enabling predictive maintenance and reduced hardware costs of the lifecycle.
Alternative Energy
Millions of such organic photovoltaic materials are being high-throughput computationally screened to find cheaper solar cell designs. AI control systems are also improving the charging rates and lifetimes of chemical reactions in a large-scale batteries.
Agriculture
AI and automation are allowing ultra-precise application of fertilizers and pesticides based on computational models. This supports efficiency and yield gains while reducing ecological impacts. Customized nutrient blends tuned to local soil and weather conditions are being designed by AI to improve plant health.
Medicine & Biotechnology
AI-designed drugs personalized to patients’ genetics and disease profiles are entering clinical trials. Automated labs powered by AI robots are accelerating everything from genetic engineering to antibody discovery. AI-directed quality control also shows promise for catching contamination in small batch biologic drugs.
The above highlights are only a glimpse of the transformative potential as two exponentially advancing fields fuse together. We are still early in exploring synergies between chemical science and artificial intelligence.
Challenges Ahead
Fully realizing the promise at the chemistry-AI nexus will require overcoming key challenges:
- Data infrastructure is lacking in chemistry. Unlike images, videos and text – data types that fueled historic AI breakthroughs – there are few large-scale chemical datasets available to train machine learning models. Centralized data platforms must be built.
- Chemistry data lacks standardization. With data locked up in disparate formats across industry and academia, aggregating datasets is hugely complex. Data interoperability standards are critically needed.
- Insufficient collaboration and knowledge sharing slow progress. Researchers remain siloed between industry and academia, while interdisciplinary literacy between chemists and AI experts is rare. Communication and collaboration must be facilitated.
- Ethical risks around dual-use technologies must be addressed. If viral strains, narcotics and toxins can be engineered autonomously in lab technologies, then there is need for preventative measures to ensure safety and preserve impact.
Alternative models for the collaborative data infrastructure to fuel advances include the Materials Acceleration Platform (MAP) from the US Department of Energy. Key to that is giving policy discussions the power to guide the development of chemistry-AI technologies towards beneficial outcomes.
Outlook for the Future
It’s only by the quarry with chemistry and artificial intelligence - they’re two exponential fields with so much synergy - it’s only now worth really starting to understand the possibilities at the confluence of chemistry and artificial intelligence. Together, the two fields can be combined alchemically to gain enormously from each other’s capabilities and insights.
Hybrid human AI chemical discovery could go into a Cambrian explosion in the next decade. With increasingly powerful AI assistants that are tightly coupled with human researchers, it is possible that the rapid emergence of innovations previously impossible to imagine is possible. There are also discoveries on clean energy, nanotechnology, run agriculture and public health with massive potential impacts.
It is time for leaders from across industry, academia and government to speedily address the barriers keeping the pace of progress at this powerful intersection in the industry. Already, our creation of the seeds of a new dawn of scientific progress - but only in concert - begin to germinate chemistry and AI into a powerful flowering.
