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Optimal decision-making in high-throughput virtual screening pipelines.

Hyun-Myung Woo¹, Xiaoning Qian^2,3, Li Tan³

¹Department of Biomedical & Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.

Patterns (New York, N.Y.)

|November 30, 2023

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This summary is machine-generated.

This study introduces an optimal framework for high-throughput virtual screening (HTVS) using multi-fidelity models. It accelerates screening by efficiently allocating computational resources, balancing accuracy and speed.

Area of Science:

Computational chemistry
Materials science
Drug discovery

Background:

Efficient screening of molecular candidates is crucial for drug discovery and materials design.
Large search spaces and high-fidelity model costs hinder practical screening.

Purpose of the Study:

To develop a general framework for constructing and optimizing high-throughput virtual screening (HTVS) pipelines.
To optimally allocate computational resources among multi-fidelity models for improved efficiency.

Main Methods:

Proposed a framework for HTVS pipelines utilizing multi-fidelity models.
Developed an optimal resource allocation strategy based on model cost and accuracy.
Validated the framework using simulated and real-world data.

Keywords:

HTS HTVS ROCI high-throughput screening high-throughput virtual screening pipeline optimal computational campaign optimal decision-making optimal screening return on computational investment

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Main Results:

Demonstrated significant acceleration of virtual screening processes.
Achieved acceleration without compromising predictive accuracy.
Enabled adaptive strategies to trade accuracy for computational efficiency.

Conclusions:

The proposed optimal HTVS framework enhances computational screening efficiency.
The framework offers flexibility in balancing accuracy and speed for virtual screening tasks.
This approach is valuable for accelerating discovery in fields like drug development and materials design.