LLM

OpenAI’s consumer and API chat lineup (GPT family), including multimodal and agent-style capabilities.

ChemCrow is an LLM-based agent system that augments large language models with 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. It integrates tools for organic synthesis, drug discovery, and materials design, allowing the agent to plan syntheses, analyze chemical properties, and execute complex chemical tasks through natural language interaction.

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. Here we introduce ChemCrow, an LLM-based agent that integrates 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. ChemCrow successfully plans syntheses, analyzes chemical properties, and executes complex chemical tasks through natural language interaction.

Anthropic’s Claude family of assistants and API models, emphasizing long context, safety, and agentic workflows.

CRISPR-GPT is an LLM-based agent system for automating gene-editing experiments. It leverages large language models to guide researchers through the entire CRISPR experiment workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation, making gene-editing more accessible to non-expert researchers.

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models have not been fully leveraged for automated experimental design in molecular biology. Here we present CRISPR-GPT, an LLM-based agent system for automating gene-editing experiments across the entire CRISPR workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation.

DeepSeek’s R1 / V3 and related open-weights and API models focused on reasoning, coding, and efficiency.

Google DeepMind’s Gemini model family for chat, search, code, and multimodal tasks across consumer and Vertex / AI Studio APIs.

Zhipu AI’s ChatGLM / GLM family of bilingual LLMs and coding assistants, with open and commercial variants.

xAI’s Grok models integrated with X (Twitter) and standalone apps, aimed at real-time, conversational assistance.

Moonshot AI’s Kimi chat and model lineup, known for long-context reasoning and Chinese–English bilingual use.

Meta’s open-weights Llama family for research and product fine-tuning, from dense LLMs to multimodal stacks.

A natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation for Mendelian diseases via structured tool access (MCP). MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. A 20B-parameter model achieved 94% pass rate on 100 expert-curated questions (vs 41% without MARRVEL-MCP), approaching state-of-the-art proprietary performance. Establishes context engineering as a core principle for biomedical AI.

Variant interpretation in rare diseases requires navigating multiple genomic databases, each with strict input formats, while synthesizing heterogeneous evidence. To address these usability barriers, we developed MARRVEL-MCP, a natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation via structured tool access. MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. Using 100 expert-curated questions, lightweight models (3B-20B parameters) with MARRVEL-MCP matched or outperformed larger models without tool access. A 20B-parameter model achieved a 94% pass rate, versus 41% without MARRVEL-MCP, approaching state-of-the-art proprietary performance. These findings establish context engineering as a core principle for biomedical AI and support scalable integration of LLMs with curated genomic resources.

MiniMax’s text, voice, and video model ecosystem for chat, APIs, and creative / agent applications.

Mistral AI’s open and commercial Mistral / Mixtral models for chat, code, and EU-focused deployments.

Open-weight and API language / multimodal models (Qwen family) from Alibaba, widely used in research and products.

ByteDance Seed LLM lineup (e.g. Seed 2.0 Pro/Lite/Mini, prior Seed 1.x) for chat, agents, multimodal, and production APIs—sibling to consumer products such as Doubao.