Solutions by industry and area

drug discovery

We provide the optimal infrastructure configuration and technical support to serve as a foundation for advancing AI-driven drug discovery "quickly and reliably."

Challenges of AI adoption in the drug discovery industry

In the field of drug discovery, AI is finding increasing opportunities in areas such as compound design, structural prediction, and screening. However, multiple challenges, including computing resources, model operation, data integration, and human resource skills, often intertwine, resulting in many projects remaining at the proof-of-concept (PoC) stage.

  • The difficulty of integrating and operating AI models into research flows.

  • The data is not prepared in a format suitable for learning and inference.

  • Data fragmentation between Wet Lab and Dry Lab

  • Building an AI infrastructure that meets security requirements

Overall design of the AI drug discovery platform provided by Macnica

Macnica designs and provides AI execution environments optimized for drug discovery research by combining GPUs, software, networking, and operational support.

Image of building an "AI execution pipeline" that can be integrated into the drug discovery research flow.

Building an "AI execution pipeline" that can be integrated into the drug discovery research flow.

We will build an execution pipeline that integrates the execution, training, and evaluation of AI models as a continuous process, aligned with the drug discovery research process.

An application platform that can handle data in a format suitable for learning and inference.

A platform for handling data in a way that is suitable for learning and inference.

We organize and structure unstructured and disorganized data accumulated in drug discovery research using algorithms that incorporate researchers' tacit knowledge. Through a data organization proof-of-concept (PoC), we verify the effectiveness of automation and develop a data infrastructure that can be reused in a format suitable for learning and inference, thereby improving the accuracy of generative AI utilization and research efficiency.

Image of realizing an on-premises AI platform that meets security requirements

Realizing an on-premises AI platform that meets security requirements

We will build a platform that allows for the complete application of AI within the company without sharing research data externally. Designed with security and governance requirements in mind, we will provide an AI drug discovery environment that balances safety and practicality, allowing both research and IT departments to use it with confidence.

Specific application scenarios in drug discovery

AI is increasingly being used in the field of drug discovery. Below, we introduce some application themes that can lead to actual business improvement and sophistication.

  • Advanced screening and docking

  • Prediction of protein structure and interactions

  • Candidate compound search using generative models

  • Candidate evaluation by predicting synthesizability and physical properties (ADMET)

Examples of AI solutions in the pharmaceutical industry

Here are some examples of NVIDIA solutions for AI implementation.
Through the challenges, solutions, and results, you will gain a concrete understanding of how AI can be used in the automotive industry.

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