Best AI Science Software in Japan

SciSure applies AI where it creates measurable value for scientists. Marketplace add-ons bring this into practice: an in-platform support assistant, automated data analysis, AI-assisted protocol generation, in silico toxicity prediction, and image analysis tools for cell and particle detection, plus specialized add-ons for areas like CAR-T research. Data control stays with the lab; models only see what's explicitly permitted, and outputs are built to be traceable rather than opaque. Teams can choose which AI tools to enable, keep them isolated to specific workflows, and add capability without disrupting the rest of the platform.

Conspecta is a browser-based platform for life science research. Image analysis, flow cytometry, molecular biology, sample tracking, and publication-ready figures all live in one connected workspace. Each result is linked to where it came from, so any number traces back to its sample, images, and analysis in one click. Image analysis pipelines count cells and colonies and measure area and intensity. Flow cytometry covers gating, compensation, UMAP, and clustering, with FlowJo and Gating-ML import and export. Inventory, orders, freezer maps, instrument booking, protocols, a lab notebook, and live project reports sit in the same workspace. Labs connect Claude, ChatGPT, or Copilot over MCP, and their own scripts through the REST API. Records import from CSV, Excel, and Word, so a lab can move over while its current system keeps running. Free for one scientist and one collaborator, and paid plans open it to a whole lab.

Edison Analysis is a next-generation scientific data-analysis agent built by Edison Scientific. It is the analytical engine underpinning their AI Scientist platform, Kosmos, and it’s available both on Edison’s platform and via API. Edison Analysis performs complex scientific data analysis by iteratively building and updating Jupyter notebooks in a dedicated environment; given a dataset plus a prompt, the agent explores, analyzes, and interprets the data to provide comprehensive insights, reports, and visualizations, very much like a human scientist. It supports execution of Python, R, and Bash code, and includes a full suite of common scientific-analysis packages in a Docker environment. Because all work is done within a notebook, the reasoning is fully transparent and auditable; users can inspect exactly how data was manipulated, which parameters were chosen, how conclusions were drawn, and can download the notebook and associated assets at any time.

NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.

ESMC is the latest in the ESM family of protein language models, establishing a new frontier in representation learning for protein biology. Trained on billions of evolutionary sequences, it learns representations that reflect a mechanistic reduction of protein structure and function. The model is built on a transformer architecture, supports sequences as its core modality, and is trained on up to 6 billion proteins. ESMC is designed for protein science research, including structure prediction, function annotation, protein design, and understanding evolutionary relationships between proteins. It can generate novel proteins from partial sequence, structure, or functional constraints, helping researchers explore new possibilities in protein design and biological discovery. The Biohub Platform provides access to ESMC through the API and the ESM Python package, with quickstart resources for installing the package, creating an API key, connecting to the platform.

ESMFold2 is the successor to ESMFold, setting a new state of the art for single-sequence structure prediction and enabling the generation of new functional proteins through searching the ESMC model’s latent space. The model predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. It is designed for structure prediction using sequence and structure modalities, with ESM representations powering a series of looped folding layers and a diffusion model projecting pairwise representations to atomic-resolution predictions. ESMFold2 predicts protein structures directly from amino acid sequences and outputs comprehensive structural information, including all-atom coordinates for backbone and side chains, confidence metrics, and optional distogram predictions for detailed structural analysis.

Charlie is a sovereign AI scientific agent for researchers and laboratories, specialized in biomedical research and built to accelerate scientific work with precise, sourced answers. It helps users conduct literature reviews, analyze scientific documents, organize research information, and move faster through R&D workflows while keeping traceability at the center of every response. Researchers can ask questions in natural language and get answers extracted directly from scientific documents, with page-level citations that make each source verifiable. Charlie can search simultaneously through hundreds of PDFs, synthesize information, compare articles, understand scientific context, and help users focus on discoveries instead of manual document review. Its research workspace includes libraries, projects, notes, shared collections, PDF reading, highlighting, annotation, and team collaboration, so important passages, references, and insights stay organized across devices.

Scispot powers life science labs with a unified LabOS™ platform, combining ELN, LIMS, SDMS, QMS, and AI in one configurable, no-code system. Built for CROs, Molecular Diagnostics, Pathology, Pharma QC, and Drug Discovery, Scispot streamlines sample management, inventory automation, and regulatory compliance. Connect with 200+ lab instruments and thousands of apps to eliminate data silos while maintaining FDA 21 CFR Part 11, GxP, GDPR, and HIPAA compliance. Scispot's AI tools transform experimental data into actionable insights, with flexible workflows that adapt as research evolves—without IT support. Trusted by 1000+ scientists globally, Scispot enables rapid deployment so teams focus on science, not administration. Accelerate discoveries, ensure compliance, and scale operations with a platform purpose-built for modern biotech innovation.

ChemCopilot is an AI-native chemical formulation and product lifecycle management platform designed to transform how scientists, engineers, and R&D teams design, test, optimize, and manage chemical products and processes by combining advanced artificial intelligence with domain-specific chemistry knowledge, regulatory data, simulation capabilities, and real-time insights. It automates validation of product labels, ingredient restrictions, and safety data sheets against global compliance frameworks, eliminating disconnected spreadsheets and manual review while providing audit trails and real-time alerts to support regulatory adherence. ChemCopilot accelerates innovation by simulating chemical reactions, molecular interactions, and process workflows to predict formulation performance and outcomes that traditional general-purpose tools cannot provide, and it integrates real-time data from laboratory and industrial systems to drive data-driven decisions.

Alchemite provides AI-augmented physical modeling and solutions that help organizations extract actionable insights from experimental and simulation data by combining machine learning with physics-informed models to improve prediction accuracy, reduce experimental costs, and optimize product and process development. Its solutions span materials discovery and design, predictive modelling of performance and reliability, multiscale modelling that connects atomistic to macroscopic behaviour, and automation of workflow tasks such as data integration, surrogate modelling, and model validation. It supports physics-aware neural networks and hybrid modelling approaches that respect underlying scientific laws while learning from data to enable faster and more accurate simulations, reduced reliance on expensive physical testing, and improved decision-making. Intellegens’ tools are applied in areas such as battery performance prediction, chemical process optimization, etc.

GPT-Rosalind is a purpose-built frontier reasoning model developed by OpenAI to accelerate scientific research across biology, drug discovery, and translational medicine. It is designed specifically for life sciences workflows, where researchers must navigate large volumes of literature, experimental data, and specialized databases to generate and validate new ideas. It combines deep domain understanding in areas such as chemistry, genomics, protein engineering, and disease biology with advanced tool-use capabilities, allowing it to interact with scientific databases, analyze experimental outputs, and support complex, multi-step reasoning tasks. It can assist with evidence synthesis, hypothesis generation, literature review, sequence interpretation, and experimental planning, helping scientists move faster from raw data to actionable insights. GPT-Rosalind transforms complex, time-intensive research processes into more efficient AI-assisted workflows.

Genome Computer turns your genetic data into a portable, AI-ready .genome bundle you can download, keep, self-host, and explore with Genome Intelligence, Codex, Claude Code, Cursor, or any compatible tool. The open format restructures the same underlying data found in a VCF into a structured, queryable bundle, with variants stored in fast columnar tables alongside trait associations, supporting research, gene-level context, polygenic scores, pharmacogenomics, and clear provenance. Whole-genome sequencing orders are built from gVCF data, preserving both detected variants and confidently sequenced regions where no variant was found, while FASTQ files are available on request. Existing VCF or TXT files from other providers can also be converted, imputed where needed, annotated, scored, and prepared for AI interpretation. Genome Intelligence lets you ask questions grounded in your actual genetic data, compare new research with your genotypes, and explore genetics.

ResoluteAI's secure platform lets you search aggregated scientific, regulatory, and business databases simultaneously. Combined with our interactive analytics and downloadable visualizations, you can make connections that lead to breakthrough discoveries. Nebula is ResoluteAI's enterprise search product for science. We apply structured metadata and a range of AI capabilities to your institutional knowledge. This includes NLP, OCR, image recognition, and transcription, making your proprietary information easily findable and accessible. With Nebula, you have the power to unlock the hidden value in your research, experiments, market intelligence, and acquired assets. Structured metadata created from unstructured text, semantic expansion, conceptual search, and document similarity search.

Claude Science is an AI-powered scientific research application that helps researchers perform data analysis, literature review, computational workflows, and manuscript preparation within a single environment. Built on Claude models, the application integrates scientific databases, research tools, electronic lab notebooks, HPC systems, and domain-specific software to support end-to-end research workflows. It manages computational environments across local machines, Linux systems, and high-performance computing clusters while maintaining reproducible records of every analysis. Researchers can generate publication-quality figures, perform complex analyses, and trace every result back to the underlying code, environment, and conversation. Claude Science also supports specialized fields including genomics, proteomics, single-cell biology, structural biology, and cheminformatics through preconfigured scientific capabilities.

The most advanced model from Google DeepMind, Gemini 3, sets a new bar for model intelligence by delivering state-of-the-art reasoning and multimodal understanding across text, image, and video. It surpasses its predecessor on key AI benchmarks and excels at deeper problems such as scientific reasoning, complex coding, spatial logic, and visual-/video-based understanding. The new “Deep Think” mode pushes the boundaries even further, offering enhanced reasoning for very challenging tasks, outperforming Gemini 3 Pro on benchmarks like Humanity’s Last Exam and ARC-AGI. Gemini 3 is now available across Google’s ecosystem, enabling users to learn, build, and plan at new levels of sophistication. With context windows up to one million tokens, more granular media-processing options, and specialized configurations for tool use, the model brings better precision, depth, and flexibility for real-world workflows.

Biohub is an open platform for building on the world model of protein biology. It provides access to the ESM family of models, including ESMC, ESMFold2, and ESM3, along with interactive tools and developer resources for protein science research. ESMC is a state-of-the-art protein language model trained on billions of evolutionary sequences, building representations that capture fundamental mechanisms of protein structure and function. It powers functional analysis, structure prediction, protein design, and the exploration of evolutionary relationships between proteins. ESMFold2 predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. ESM3 jointly models sequence, structure, and function, enabling controllable generation of novel proteins by conditioning on any combination of these modalities.

Voquill is the agentic OS for medical labs, built AI-native to run the lab’s core workflow from accessioning and case routing to reporting and sign-out, with agentic AI built into every step. Instead of forcing modern labs to work around legacy systems that do not scale or plug into today’s AI, Voquill provides a modern LIS where every stage of the case has an agent working alongside the team. For accessioning, agents read the requisition and build the order, so intake stops being data entry. Labs can scan the paper requisition and have an agent pull patient information, tests, and specimen details into a structured order, while the team reviews and approves instead of retyping. The original scan is kept forever, with a confidence score on every extracted field. For case distribution, cases route themselves to the right pathologist by subspecialty, current workload, and turnaround pressure, without a rules engine to babysit or morning triage to manage.