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Neotrident NEO-ATM: Empowering Precision Drug Design with High-Efficiency, High-Accuracy Binding Free Energy Calculations

by CompanyNews Center

In drug discovery, the binding free energy (ΔG) between a target protein and a small molecule is a critical parameter for evaluating drug activity. Traditional computational methods, limited by insufficient sampling and model oversimplification, often struggle to balance accuracy and efficiency. The NEO-ATM module from Neotrident, leveraging advanced molecular dynamics (MD) and enhanced sampling techniques, overcomes these traditional bottlenecks to deliver high-precision, reliable relative binding free energy predictions, providing a scientific foundation for drug optimization.

Introduction of NEO-ATM

1. Relative Binding Free Energy (RBFE): Mitigating Errors in Absolute Calculations

Traditional absolute binding free energy calculations require simulating receptor conformational changes from scratch, which are prone to errors due to insufficient sampling. NEO-ATM employs relative binding free energy calculations, keeping ligands fixed within the binding pocket to avoid errors caused by receptor conformational drift, significantly improving result stability.

2. Enhanced Sampling with H-REMD: Breaking Time-Scale Barriers

Using Hamiltonian Replica Exchange Molecular Dynamics (H-REMD), NEO-ATM efficiently explores dynamic conformations of ligand binding pockets, capturing transient interactions invisible in short simulations. This reduces simulation time by over 30% while delivering results closer to experimental values.

3. Multi-Replica Parallel Computing: Reducing Statistical Uncertainty

NEO-ATM defaults to running three independent replica simulations, ensuring reliability through consistency validation. In high-variability systems (e.g., 5ns simulations), standard deviation is reduced by 40% compared to single simulations.

Case Study: Binding Free Energy Prediction for PKMYT1 Inhibitors

Background

Protein kinase PKMYT1 is a key cancer therapeutic target. Its binding mechanism with inhibitor RP-6306 directly impacts drug efficacy. Using co-crystal structures of PKMYT1 with two inhibitors (QGY, QG1; PDB ID: 8D6D/8D6E), we validated the accuracy of NEO-ATM’s ΔG predictions.

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Methods

  1. Receptor Preparation

Protonation states optimized with PROPKA3.1; hydrogen bond networks completed via PDB2PQR.

  1. Ligand Preparation

Conformational optimization performed on the MaXFlow platform.

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  1. Complex Construction

Ligands aligned to co-crystal binding sites using rigid alignment to ensure binding mode consistency.

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  1. NEO-ATM Workflow Setup

Three replica simulations to minimize statistical error and ensure reproducibility.

  1. Simulation Parameters
  2. Results Analysis

Key Findings

  1. Strong Agreement with Experimental Data

NEO-ATM’s 3ns and 5ns simulations both showed excellent alignment with experimental data. RMSE and MAE fell within chemical accuracy (1 kcal/mol), with 5ns results further validating the module’s precision.

  1. Data Robustness Confirmed

High-quality crystal structures and experimental data were validated as reliable inputs for NEO-ATM predictions.

  1. Adaptability of NEO-ATM

The module excelled in capturing key target-ligand interactions for protein Y, laying a foundation for simulations without experimental baselines.

  1. Client Value: Accelerating Drug Discovery, Reducing Risk

Precision Optimization: Rank candidates via ΔΔG to prioritize high-affinity compounds, reducing experimental trial costs.

Early Decision-Making: Predict binding modes without experimental data, shortening lead compound discovery cycles.

Cross-Target Applicability: NEO-ATM supports over 100 protein targets, enabling rapid migration to new projects.

Empowering Innovation with Science

Creaton Technology’s NEO-ATM module provides end-to-end support for drug discovery—from target validation to optimization—through high-accuracy, efficient binding free energy calculations. Its success in the PKMYT1 case, with results closely matching experiments, demonstrates adaptability in complex systems. Moving forward, Creaton will advance AI-driven computational biology to overcome “undruggable” targets and accelerate precision medicine.

Science-Driven Intelligent Data Engine

With 20 years in pharmaceutical and materials science R&D, Neotrident empowers enterprises and research institutions to achieve digital transformation through three proprietary platforms powered by cloud computing, mobile connectivity, and scientific AI:

iLabPower R&D Platform: Manages end-to-end R&D data to ensure authenticity, integrity, and traceability. Reduces costs, boosts efficiency, and safeguards intellectual property.

SDH Scientific Data Hierarchy: Accelerates cross-source data integration and analysis to shorten time-to-market and enhance product quality.

MaXFlow Molecular Simulation & AI Platform: Democratizes molecular modeling and AI-driven innovation, replacing traditional trial-and-error approaches with data- and model-driven discovery.

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iLabPower BIMS V2.6 Ushers in the Era of “Comprehensive Visual Management” for Laboratory Animals

by CompanyNews Center

A breakthrough in laboratory animal management is here! The all-new iLabPower BIMS V2.6 has officially launched, featuring key upgrades to its animal management module. Designed for non-rodent species like monkeys, dogs, and pigs, it enables end-to-end digitization and visualization—from cage intake and daily management to experimental workflows—delivering a more intuitive and efficient experience for research teams!

Highlights at a Glance:

Visual Layouts Tailored to Your Animal Facility
Customize cage layouts and position-coding rules for different species. Design cage distributions freely with a “what-you-see-is-what-you-get” interface, effortlessly adapting to diverse animal facility configurations.

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Batch Cage Assignments via Drag-and-Drop or Barcode Scanning
Assign animals to cages instantly through visual drag-and-drop or barcode scanning. Switch seamlessly between list and graphical views for clarity and doubled efficiency.

Enhanced Cage Management Optimized for Non-Rodent Species
Track animal profiles, lifecycles, and experimental participation from multiple angles. Automatically summarize real-time statuses and perform cage transfers or disposals with intuitive visual controls.

Flexible Workflows for Diverse Scenarios
Manage animal statuses in bulk—enroll in experiments, transfer between departments, or dispose—to align with varying experimental protocols.

Dedicated Management for Project-Specific Animals
Independently track animals by research project, log experimental dosing data, and automate post-study transitions (e.g., reserve status with washout period calculations).

Customizable Record Querying & Exporting
Flexibly search and export records (lifecycle, cage transfers, disposals, department changes) with customizable fields, ensuring seamless cross-department collaboration and audit trails.

Mobile Barcode Operations for On-the-Go Management
Leverage Pad browsers with NFC compatibility to scan chips, assign cages, relocate animals, or swap cages—all from the facility floor. Break spatial constraints and streamline workflows.

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NeoTrident Achieves ISO27001 Certification, Internationally Recognized for Information Security Management!

by CompanyNews Center

NeoTrident, along with its parent company, Suzhou NeoTrident Software Co., Ltd., passed the ISO/IEC 27001:2013 certification from the British Standards Institution (BSI). This milestone marks NeoTrident’s internationally recognized capability in information security management, strengthening its position as a secure and reliable IT provider in life and material sciences.

 

This certification reinforces NeoTrident’s commitment to safeguarding customer data and system security, further enhancing its product offerings like the iLabPower Collaborative Innovation Cloud. The certification affirms that NeoTrident adheres to rigorous international standards in areas such as data encryption, asset management, and system security. The company continues to prioritize security management, fostering customer trust and ensuring robust data protection across its digital platforms.

 

With over 1,000 R&D institutions using NeoTrident’s solutions, the ISO27001 certification adds credibility to their extensive service portfolio in fields ranging from research to production.

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NeoTrident’s iLabPower SEQ Sequence Editor: Accelerating Bio-New Drug Development

by CompanyNews Center

With the rise of electronic lab notebooks, molecular biologists and chemists are now able to collaborate more efficiently, streamlining the development of new drugs through standardized workflows. However, as the life sciences industry continues to evolve, there are several challenges that researchers face when working with increasingly complex data and processes in bio-new drug development. One of the main challenges is the need for specialized software solutions that can handle the growing demands of sequence analysis, data management, and experimental reproducibility.

Biological Sequence Editor

Data Complexity
As drug development processes become more sophisticated, the amount of data generated from biological experiments has increased significantly. Managing and analyzing this data efficiently is crucial for timely and accurate results. Complex workflows, such as gene editing, protein synthesis, and antibody development, require software tools that can handle large datasets, perform detailed analyses, and ensure data traceability across multiple experiments and teams.

R&D Costs and Efficiency
Developing new drugs requires substantial investment in research and development. For laboratories and pharmaceutical companies, optimizing the efficiency of R&D processes is critical to reducing costs and shortening development timelines. Time-consuming tasks, such as manually curating sequences, calculating biochemical properties, and designing primers, can slow down research and lead to errors. Streamlining these processes with integrated tools is essential to improve productivity and ensure high-quality outcomes.

Some researchers have tried using a variety of standalone tools to manage data, but this approach often leads to fragmented workflows, data silos, and inconsistent results, ultimately wasting valuable time and resources.

To address these challenges, NeoTrident offers a solution: the iLabPower SEQ Sequence Editor. This tool, part of the iLabPower R&D management platform, provides researchers with an integrated and efficient platform for biological sequence design and analysis. Equipped with visual sequence editing functionality, the tool also allows researchers to document the design process seamlessly in the electronic lab notebook, ensuring full traceability and reproducibility.

iLabPower SEQ Sequence Editor Overview
The iLabPower SEQ Sequence Editor enables users to manage the entire sequence design process—from sequence creation and editing to validation—all within a single platform. For example, in plasmid construction, a common molecular biology experiment, essential steps such as gene and vector selection, PCR primer design, pre-assembly cutting with appropriate endonucleases, and verification via sequencing results can all be conducted within the Sequence Editor. By consolidating these workflows, the platform ensures traceability and reproducibility, which are essential for reliable research. Moreover, applications like CRISPR and circular RNA construction follow similar workflows, making iLabPower SEQ Sequence Editor versatile for a range of bio-design processes.

Key Features of iLabPower SEQ Sequence Editor

  1. Sequence Creation: The editor supports various ways to create sequences:
    • Import FASTA, GenBank, and other annotated sequence files.
    • Customize sequence labeling for efficient management and searching.
    • Automatically process sequences, including case conversion, coordinate generation, and sequence editing operations, saving time and reducing manual errors.
  2. Visual Sequence Display: The editor allows users to visualize sequences as linear or circular maps, displaying annotations, primers, and restriction sites in a personalized format. This makes it particularly useful for designing structures such as plasmids, CRISPR, and miRNA.
  3. Nucleic Acid Sequence Analysis:
    • DNA Property Calculation and Primer Design:
      Automatically calculates properties like annealing temperature, GC content, and sequence length for primer design. The built-in Primer3 tool generates primer pairs based on user-specified parameters.
    • Enzyme Site Display:
      Identifies restriction enzyme sites using the NEB-Enzyme Finder and displays them for faster sequence editing.
    • Open Reading Frame (ORF) Recognition:
      The editor automatically identifies ORFs, supporting gene expression analysis, which is crucial for drug development.
  4. Amino Acid Sequence Analysis:
    • Protein Biochemical Characterization:
      Automatically translates DNA sequences into amino acids, enabling analysis of protein properties, including hydrophobic regions, isoelectric points, and molecular weight. It also facilitates antibody design by identifying CDR (Complementarity-Determining Region) regions critical for antigen recognition.
  5. Comparative Analysis:
    • BLAST:
      Provides one-click submission of sequence fragments to NCBI’s BLAST tool for functional prediction.
    • Dual Sequence Alignment:
      Supports alignment of DNA or amino acid sequences, helping users visualize conserved regions and sequence variations.
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iLabPower SEQ Sequence Editor in Bio-New Drug Development

New biopharmaceuticals have shown tremendous promise in treating diseases once deemed incurable, such as cancer and genetic disorders. With its intuitive, efficient, and secure design, iLabPower SEQ Sequence Editor offers a new approach to innovative research in laboratories and pharmaceutical companies. From upstream design to downstream validation, all sequence-related tasks can be conducted on a single platform with robust data security and reproducibility.

Moreover, NeoTrident is committed to continually upgrading the software, improving user interaction, and expanding its bioinformatics capabilities to better serve the bio-new drug R&D landscape. By addressing the unique needs of researchers, NeoTrident aims to support intellectual property protection and accelerate the pace of innovation in biopharmaceuticals.

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AI Application in Drug Development: Outsourcing or Autonomous Mastery?

by IndustryNews Center

In recent years, the application of Artificial Intelligence (AI) in drug discovery and development (D&D) has become increasingly prominent. AI has significantly improved the efficiency of drug research by predicting drug efficacy and toxicity, automating the design of drug molecules, and accelerating clinical trials. Faced with the impact and opportunities brought by AI, pharmaceutical companies usually take one of three approaches: developing AI capabilities internally, purchasing mature and specialized AI platforms, or outsourcing AI-driven drug discovery and development (AIDD) to third-party providers. But which strategy is the best?

 

Is Fully Outsourcing Your AIDD a Safe Bet?

Many companies opt to outsource their AIDD efforts, as it seems to save time and reduce costs. However, this approach presents some significant challenges.

First, data security is a major concern. Data is the lifeblood of drug discovery, and a leak could cause severe damage to a company’s business. Once data is outsourced, it becomes difficult to ensure its complete protection. Secondly, data ownership is equally crucial. Outsourcing may result in a company losing ownership of vital data, which could weaken its competitive edge. Furthermore, the quality of services provided by outsourcing companies can vary widely. If an unreliable partner is chosen, it may slow down R&D progress or lead to inaccurate research results.

Therefore, outsourcing AIDD could lead to higher long-term costs. On one hand, outsourcing expenses may increase over time as data grows; on the other hand, the financial losses due to security or service issues might exceed initial savings.

Why Emphasize Autonomous Mastery of AI Tools and Data?

While achieving autonomous control over AI tools and data may require a larger investment in the short term, it offers considerable long-term benefits.

First, professional drug researchers are more likely to achieve better results using AI tools than external partners who may lack a deep understanding of drug research. Internal teams can comprehend research objectives more thoroughly and use AI more effectively. Additionally, owning AI tools and data ensures that a company’s intellectual property and data assets are protected, securing data ownership and preventing security breaches. Furthermore, autonomous control provides greater flexibility. Companies can adapt AI tools to their specific needs and quickly adjust to changes in R&D strategies without waiting for an outsourcing partner’s response. This approach also helps build and retain AI expertise within the company. As AI becomes a core tool in drug development, companies that possess in-house expertise will have a clear advantage. Finally, controlling AI tools and data helps ensure consistency and quality, avoiding errors related to data transfers and conversions.

In the long run, owning AI tools and data can also be more cost-effective. Once a company establishes its own AI platform, it only needs to cover maintenance costs instead of recurring, often escalating, outsourcing fees.

Does Autonomous Mastery Mean Developing AI Platforms Internally?

Autonomous mastery of AI tools doesn’t necessarily require developing them from scratch. In fact, purchasing a mature, low-barrier AI platform from a specialized provider is often a more efficient and cost-effective solution.

To facilitate the integration of “AI+Pharmaceuticals” and empower drug R&D companies to easily and independently manage AI tools and data, Trunetech offers an advanced, user-friendly AI platform called MaXFlow.

MaXFlow, developed by Neotrident, is a next-generation molecular simulation and AI innovation platform aimed at all frontline experimental scientists, computational simulation experts, and AI specialists. It covers a wide range of R&D fields, including innovation discovery and process development. MaXFlow enables users to perform drug design, predict drug properties, manage research data, and much more. Its ease of use ensures that even scientists with no prior experience in molecular simulation or AI can quickly get started.

  • Easily build 3D models of molecules, proteins, nucleic acids, etc.
  • Enhance modeling efficiency through component and workflow technology
  • Connect with the SDH scientific data genome platform to improve data acquisition
  • Open environment that integrates and encapsulates various algorithms for full algorithmic freedom
  • SaaS cloud model that seamlessly connects with background supercomputing resources to ensure computational power availability
  • Extensive APP resources for practical application scenarios, allowing molecular simulation and AI technologies to be accessible with zero barriers
  • Sharing of physical/AI models to capture expert experience and knowledge
  • A platform-level solution that provides robust file management, user role, and permission management features to ensure data security, internal knowledge transfer, and the accumulation of valuable data assets.

Conclusion

Overall, taking ownership of AI tools and data is a wise and strategic choice for drug development companies in the long term. This approach not only safeguards a company’s data assets and intellectual property, enhances flexibility, and nurtures in-house talent, but also reduces costs over time. Partnering with a professional AI platform provider is an effective way to achieve this goal. High-quality AI platforms are not only mature and easy to use but also provide strong support, enabling drug development companies to better leverage AI technology.

In the “AI+Pharmaceuticals” field, both IT professionals and pharmaceutical researchers face challenges. Regardless of the chosen strategy, clear communication and collaboration between these teams are critical. Every drug development company should carefully evaluate its AI strategy, consider whether there are better options, and determine how to use AI more efficiently to unlock greater value.