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Updated: Jun 25, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

Changdai Gu^1,2, Woo Dae Jang^3,4, Kwang-Seok Oh^3,4

¹Artificial Intelligence Laboratory, Oncocross Co., Ltd., Saechang-ro, Mapo-gu, Seoul 04168, Republic of Korea.

Computational and Structural Biotechnology Journal

|May 29, 2024

Summary

AnoChem is a new deep learning framework that assesses the chemical realism of generated molecules for drug discovery. It helps identify realistic compounds, improving the efficiency of *de novo* drug design.

Keywords:

AnoChem Cheminformatics Computational chemistry Drug design Machine learning

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Published on: October 20, 2021

Area of Science:

Computational Chemistry
Drug Discovery
Artificial Intelligence in Chemistry

Background:

*De novo* drug design aims to discover novel compounds efficiently.
Generative models often produce non-synthesizable or unrealistic molecules.
Accurate assessment of generated chemical structures is crucial for drug design.

Purpose of the Study:

To present AnoChem, a deep learning framework for assessing the realism of generated molecules.
To evaluate the performance of generative models using AnoChem.
To provide a reliable tool for *de novo* drug design.

Main Methods:

Developed a deep learning computational framework named AnoChem.
Trained AnoChem to distinguish between real and generated molecules.
Evaluated AnoChem's performance using Area Under the Receiver Operating Characteristic Curve (AUC).
Compared AnoChem with other metrics like SAscore and Fréchet ChemNet distance (FCD).

Main Results:

AnoChem achieved an AUC score of 0.900 in distinguishing real from generated molecules.
AnoChem demonstrated a strong correlation with SAscore and FCD metrics.
The framework effectively validated the performance of various generative models.

Conclusions:

AnoChem is a reliable computational tool for assessing the chemical realism of generated molecules.
This framework can significantly aid *de novo* drug design by filtering unrealistic compounds.
AnoChem enhances the development and evaluation of generative models for drug discovery.