Topological Similarity Search in Large Combinatorial Fragment Spaces

Self Cite

The distributions of physicochemical property values, like the octanol–water partition coefficient, are routinely calculated to describe and compare virtual chemical libraries. Traditionally, these distributions are derived by processing each member of a library individually and summarizing all values in a distribution. This process becomes impractical when operating on chemical spaces which surpass billions of compounds in size. In this work, we present a novel algorithmic method called SpaceProp for the property distribution calculation of large nonenumerable combinatorial fragment spaces. The novel method follows a combinatorial approach and is able to calculate physicochemical property distributions of prominent spaces like Enamine’s REAL Space, WuXi’s GalaXi Space, and OTAVA’s CHEMriya Space for the first time. Furthermore, we present a first approach of optimizing property distributions directly in combinatorial fragment spaces.

Section: ■ Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

Bellmann

Klein²,

Rarey

2022

Self Cite

“…Then the combined similarity score is calculated for the resulting combinations of fragments as a weighted sum of fragment scores and the list of the highest-ranked combinations is returned. The high correlation to a classic sequential fingerprint-based similarity search on the enumerated product space was shown by Bellmann et al 50 SinthI uses a different approach. For a given query synthon, SinthI searches the analogue synthons using Tanimoto threshold in order to control the minimal similarity.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

SynthI: A New Open-Source Tool for Synthon-Based Library Design

Zabolotna

Volochnyuk

Gavrylenko

et al. 2021

Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github. com/Laboratoire-de-Chemoinformatique/SynthI.

“…20,21 Already in their current form, make-on-demand compound spaces are too large for enumeration requiring combinatorial fragment representations (Fragment Spaces) and custom-made search algorithms. 16,22 The FTrees-FS algorithm was the first of its kind enabling the exact search with a reduced graph similarity measure. 22 T h i s c o n t e n t i s torial search approach based on connected substructure fingerprints (CSFPs) 16 and Morgan fingerprints highly similar to the well-established ECFP 14 fingerprints.…”

Section: ■ Introductionmentioning

confidence: 99%

“…16,22 The FTrees-FS algorithm was the first of its kind enabling the exact search with a reduced graph similarity measure. 22 T h i s c o n t e n t i s torial search approach based on connected substructure fingerprints (CSFPs) 16 and Morgan fingerprints highly similar to the well-established ECFP 14 fingerprints.…”

Section: ■ Introductionmentioning

confidence: 99%

Maximum Common Substructure Searching in Combinatorial Make-on-Demand Compound Spaces

Schmidt

Klein²,

Rarey

2021

Self Cite

Commercial make-on-demand compound spaces have become increasingly popular within the past few years. Since these libraries are too large for enumeration, they are usually accessed using combinatorial fragment space technologies like FTrees-FS and SpaceLight. Although both search types are of high practical impact, they lack the ability to search for precise structural features on the atomic level. To address this important use case, we developed SpaceMACS enabling efficient and precise maximum common induced substructure (MCIS) similarity and substructure searches within chemical fragment spaces. SpaceMACS enumerates a user-defined number of compounds in a multistep procedure. First, substructures of the query are extracted and matched to all fragments of the space. Then partial results are combined to actual compounds of the space. In this way, SpaceMACS identifies common substructures even if they cross fragment borders. We applied SpaceMACS on three commercial fragment spaces searching for the 150 000 most similar analogs to a glucosyltransferase binder from literature. We were able to find almost all building blocks used for the synthesis of the 90 listed analogs and a plethora of additional results. SpaceMACS is the missing link to enable rational drug discovery on make-on-demand combinatorial catalogs. No matter whether initial compound suggestions come from a de novo design, an AI-based compound generation, or a medicinal chemist's drawing board, the method gives access to the structurally closest chemically available analogs in seconds to at most minutes. Recently, SpaceLight 16 was introduced as the first combina-