CACTUS: Chemistry Agent Connecting Tool Usage to Science.

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions.

Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in .

Interactions between plants and soil microbial communities that benefit plant growth and enhance nutrient acquisition are driven by the selective release of metabolites from plant roots, or root exudation. To investigate these plant-microbe interactions, we developed a photoaffinity probe based on sorgoleone (rgoleone iazirine lkyne for hotoffinity abeling, SoDA-PAL), a hydrophobic secondary metabolite and allelochemical produced in root exudates. We applied SoDA-PAL to the identification of sorgoleone-binding proteins in SO1, a potential plant growth-promoting microbe isolated from sorghum rhizosphere soil.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods.

Bayesian Inference for Integrating Multiomics Datasets with Metabolic Modeling.

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements.