• Large Language Models Overview

A language model is like a computer program that is trained on a large dataset of text to learn the patterns and relationships between words. This allows the model to generate new sentences or predict the likelihood of a sentence being grammatically correct. A large language model is a language model that has been trained on an enormous amount of text data, sometimes consisting of billions ofwords. These models use deep learning techniques, such as neural networks, to learn the patterns and relationships between words in a way that is similar to how humans learn language. LLMs are designed to process and generate human-like text based on input provided by users, making them valuable tools for tasks such as natural language processing, machine translation, question answering, and more.

One popular example of an LLM is GPT-3, developed by OpenAI. GPT-3 is trained using reinforcement learning techniques and has been shown to outperform previous generations of LLMs in terms of accuracy and speed. 

• Tools integrated with ChemCrow

• General Tools

• WebSearch 

The web search tool is designed to provide the language model with the ability to access relevant information from the web. The tool, SerpAPI 76, is used to query search engines and gather a set of results from the first page of Google search. By doing so, the model is able to obtain up-to-date and pertinent information on a wide range of scientific subjects.

One important feature of this tool is that it helps the language model when it encounters a question it doesn't know the answer to or isn't sure how to find the information. The web search tool acts as a starting point for the model, helping it to quickly expand its knowledge base and accurately answer questions.

• LitSearch 

The primary function of the literature search tool is to extract pertinent information from scientific documents, such as PDFs or text files, including raw HTML. This enables the tool to offer precise and well-supported answers to questions. By basing its responses on established scientific literature, the tool considerably improves the model's ability to furnish dependable and accurate information for regular scientific tasks, while also citing the relevant papers.

• Python REPL 

ChemCrow is equipped with a practical Python shell that allows the LLM to write and execute Python code directly, simplifying the completion of various intricate tasks. These tasks can span from carrying out numerical computations to training AI models and conducting data analysis.

• Molecule tools

• Name2SMILES 

This tool is created to retrieve the SMILES representation of a specific molecule. It accepts the name or CAS number of the molecule as input and returns the corresponding SMILES string. This tool enables users to easily request tasks that involve molecular analysis and manipulation, by simply referring to the molecule in natural language.

• SMILES2Price 

This tool serves the purpose of providing cost of a particular molecule.

• Name2CAS 

The purpose of this tool is to identify the Chemical Abstracts Service (CAS) number associated with a specific molecule.

• ModifyMol 

This tool is created to modify a specific molecule with the help of retro and forward synthesis rules. By doing so, it allows the model to discover molecules that are structurally similar, and generate new and unique molecules. With this feature, researchers can explore novel molecular structures, derivatives, and optimize their molecular candidates for specific applications, such as drug discovery and chemical research. This tool provides a powerful toolset for exploring and manipulating molecular structures, opening up new possibilities for scientific research and discovery.

• PatentCheck 

The purpose of the patent checker tool is to determine whether a particular molecule has been patented or not.

• FuncGroups 

The tool is created to detect and identify functional groups present in a given molecule. It provides a detailed overview of the functional groups within the molecule, enabling the LLM to make informed decisions while designing experiments, synthesizing compounds, or exploring new molecular candidates. With this tool, researchers can gain a deeper understanding of the molecular properties and characteristics of the compounds they are working with, leading to more effective research and experimentation.

• Similarity 

This tool enables the model to determine and compare how similar two molecules are to each other.

• SMILES2Weight 

This tool is designed to compute the molecular weight of a molecule based on its SMILES representation. It employs RDKit to obtain the precise molecular weight from the SMILES string. 

• Safety tools

• ChemicalWeaponCheck 

This tool is designed to minimize the risk of dangerous chemical substances being used improperly. It does this by checking a molecule's CAS number against various lists of known Chemical Weapons and Precursors. Whenever a request is made to modify or synthesize a molecule, the tool is automatically activated. If the molecule is found on any of these lists, which suggests it could be a chemical weapon or precursor, the tool immediately stops the process. This tool provides essential safety information to help users make informed and safer decisions.

• ExplosiveCheck 

This tool is used to identify molecules that are explosive. To do this, it searches the PubChem database using molecular identifiers like common name, IUPAC name, or CAS number. If the molecule is rated as "Explosive", the tool confirms that it is indeed explosive. Whenever a user requests a synthesis method, the ChemCrow tool automatically activates this feature, providing an appropriate warning or error message to the user if the molecule is identified as explosive. This helps to minimize the risk of accidents and other hazards associated with the use of explosive substances.

• SafetySummary 

This tool gives a general overview of the safety of any given molecule. 

• Chemical reaction tools

• NameRXN

The tool, which is operated by NextMove Software's proprietary software NameRxn, is developed to recognize and categorize a specific chemical reaction using its internal database of numerous named reactions. This information is crucial for comprehending reaction mechanisms, choosing suitable catalysts, and refining experimental conditions.

• RXNPredict 

The reaction prediction tool makes use of the RXN4Chemistry API developed by IBM Research, which employs a transformer model specially designed to predict chemical reactions and retrosynthesis pathways based on the Molecular Transformer. This results in extremely precise predictions. By providing a set of reactants as input, the tool generates the anticipated product, offering the LLM with precise chemical information that is not always obtainable through a basic database query, but rather requires the sort of abstract reasoning that chemists are trained to perform. Although the API is available free of charge, registration is mandatory.

• RXNPlanner 

This potent tool also integrates the RXN4Chemistry API from IBM Research, utilizing the Transformer for translation tasks, like the reaction prediction tool. However, it also includes search algorithms for handling multi-step synthesis and an action prediction algorithm that transforms a reaction sequence into machine-readable format, including conditions, additives, and solvents. The molecular synthesis planner is created to aid the LLM in devising a synthetic route for producing a desired target molecule. By providing the SMILES representation of the target product as input, this tool enables ChemCrow to generate and compare effective synthetic pathways towards the desired compound.

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