Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

Abstract

In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure−activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millionsor billionsof compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFR T790M , and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.

Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors

“…Kinase inhibitors already have a strong record as pharmaceuticals, with more than 70 unique chemical entities receiving United States Food and Drug Administration (FDA) approval and many more in development [3]. Kinase inhibitor libraries, however, are often large due to the diversity of chemical space containing canonical hinge-binding motifs [4,5]. Accordingly, combing through large kinase inhibitor libraries using traditional high-throughput screening techniques can be time consuming and costly [6], presenting a natural opportunity for contributions from complementary in silico techniques.…”

De novo generated combinatorial library design

“…We propose a library optimization workow for de novo generated building blocks in a combinatorial fashion applying recombination. 90,91 Using LibINVENT, 73 we generate and lter building blocks that can attach to an example scaffold using specied reactions. These building blocks can be both novel or previously existing in eMolecules, 92 a platform aggregating instock commercial building blocks from "over 140 suppliers".…”

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

“…Commercial catalogues are generally used for this purpose as the compounds are guaranteed to be synthetically feasible and are cheap and easy to acquire. Nevertheless, there are limited examples within the literature of formalized workflows that use catalogue searches to identify and filter compounds. , Existing methods typically employ search techniques such as substructure and similarity searching, yet these methods can be computationally intensive and fingerprint-based similarity metrics can exhibit bias against fragments (as smaller molecules occupy fewer bits).…”