SadPhos Library: A Comprehensive Resource for Exploring Chiral Ligand Chemical Space DOI
Shuang Yu

Chemistry - An Asian Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract Traditionally, the discovery of ligands for organic reactions has relied heavily on intuition and experience chemists, leading to a trial‐and‐error process that is both time‐consuming inherently biased. The rise data science now offers more systematic efficient approach exploring chemical spaces, moving beyond heuristic constraints conventional ligand design enabling data‐driven, predictive method. In this study, we introduce “SadPhos Library”, comprehensive collection 890 reported chiral sulfinamide phosphine ligands, use physical descriptors systematically map their space. By examining small dataset known active demonstrate how SadPhos library can help identify key properties associated with performance thus streamline optimization. Furthermore, employing dimensionality reduction clustering techniques, pinpoint representative subset facilitates targeted exploration diverse landscape.

Language: Английский

Dedenser: A Python Package for Clustering and Downsampling Chemical Libraries DOI
Armen G. Beck, Jonathan Fine, Yu‐hong Lam

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

The screening of chemical libraries is an essential starting point in the drug discovery process. While some researchers desire a more thorough targets against narrower scope molecules, it not uncommon for diverse sets to be favored during early stages discovery. However, cost burden associated with potential drawbacks if particular areas space are needlessly overrepresented. To facilitate triaged sampling and other collections we have developed Dedenser, tool downsampling clusters. Dedenser functions by reducing membership clusters within clouds while maintaining initial topology or distribution space. Python package that utilizes Hierarchical Density-Based Spatial Clustering Applications Noise first identify present 3D then downsamples applying Poisson disk based on either their volume density A command line interface graphic user available which allow generation clouds, using Mordred QSAR descriptor calculations uniform manifold approximation projection embedding, as well visualization. We hope will serve community enabling quick access reduced molecules representative larger selecting even distributions rather than single from All code open source at https://github.com/MSDLLCpapers/dedenser.

Language: Английский

Citations

0
Artificial Intelligence in Retrosynthesis Prediction and its Applications in Medicinal Chemistry DOI

Lanxin Long,

Rui Li, Jian Zhang

et al.

Journal of Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle adapt vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized prediction recent decades, significantly increasing accuracy diversity compounds. Single-step AI-driven models can be generalized into three types based on their dependence predefined reaction templates (template-based, semitemplate-based methods, template-free models), with respective advantages limitations, common challenges that limit chemistry applications. Moreover, there are relatively inadequate multi-step which lack strong links single-step methods. Herein, we review advancements AI applications summarizing related techniques landscape current representative propose feasible solutions tackle existing problems outline future directions this field.

Language: Английский

Citations

0
Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation DOI Creative Commons
Xinran Chen, Zijing Zhang, Xin Hong

et al.

Nature Synthesis, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Language: Английский

Citations

0
Machine learning workflows beyond linear models in low-data regimes DOI Creative Commons
David Dalmau, Matthew S. Sigman, Juan V. Alegre‐Requena

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This work presents automated non-linear workflows for studying problems in low-data regimes alongside traditional linear models.

Language: Английский

Citations

0
SadPhos Library: A Comprehensive Resource for Exploring Chiral Ligand Chemical Space DOI
Shuang Yu

Chemistry - An Asian Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract Traditionally, the discovery of ligands for organic reactions has relied heavily on intuition and experience chemists, leading to a trial‐and‐error process that is both time‐consuming inherently biased. The rise data science now offers more systematic efficient approach exploring chemical spaces, moving beyond heuristic constraints conventional ligand design enabling data‐driven, predictive method. In this study, we introduce “SadPhos Library”, comprehensive collection 890 reported chiral sulfinamide phosphine ligands, use physical descriptors systematically map their space. By examining small dataset known active demonstrate how SadPhos library can help identify key properties associated with performance thus streamline optimization. Furthermore, employing dimensionality reduction clustering techniques, pinpoint representative subset facilitates targeted exploration diverse landscape.

Language: Английский

Citations

0