Evidence map›Paper›PMID 40025591›Full record

ArticleJournal of cheminformatics2025

Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates.

Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang and 3 more

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Xiaodan YinCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Xiaorui WangCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Zhenxing WuCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Qin LiFaculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
Yu KangCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Yafeng DengCarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018, Zhejiang, China.
Pei LuoDr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, 999078, China.
Huanxiang LiuFaculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
Guqin ShiShanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai, 310115, China.
Zheng WangShanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai, 310115, China.
Xiaojun YaoFaculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China. xjyao@mpu.edu.mo.
Chang-Yu HsiehCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. kimhsieh@zju.edu.cn.
Tingjun HouCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. tingjunhou@zju.edu.cn.

Funding

National Key R&D Program of China 2021YFE0206400
6 · The paper itself

Abstract

Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt's robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt's universal workflow makes it suitable for various types of molecular optimization tasks.

Indexed as

Metabolic property optimizationMolecular optimizationReaction templateSynthetic planningToxicity optimization

Identifiers

PMID40025591
PMCPMC11874783

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