A Novel Machine Learning Approach Uncovers New and Distinctive Inhibitors for Cyclin-Dependent Kinase 9

Abstract

We present a novel combination of generative and predictive machine learning models for discovering unique protein inhibitors. The new method is assessed on its ability to generate unique inhibitors for the cancer associated protein kinase, CDK9. We validate our method by performing biochemical assays, attaining a hit rate of more than 10%, demonstrating the method to be a notable improvement upon a more standard, and somewhat naive approach. Moreover, we imposed the additional challenge of finding inhibitors that are readily synthesized. Importantly, two new inhibitors are found, with one being distinct from reported CDK9 inhibitors. We discuss the results in the context of modern machine learning principles and the desire expressed by the rational drug design community to secure molecules that are structurally different, yet with high binding affinities, to structurally determined protein targets.

Accelerating drug target inhibitor discovery with a deep generative foundation model

“…Previous generative machine learning models that have been subject to experimental validation of de novo-designed molecules were primarily either trained or fine-tuned on a target-specific ligand library (6,7,(43)(44)(45)(46)(47). This work establishes the basis for an alternative discovery paradigm, wherein a generative model is used to discover previously unidentified inhibitor hits for different protein targets in an automated fashion.…”

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework

“…Previous generative machine learning models that have been subject to experimental validation of de novo-designed molecules were primarily either trained or fine-tuned on a target-specific ligand library 6,7,[36][37][38][39][40] .…”

“…In the scenario of designing inhibitors for a new target, a sufficient amount of exemplar molecules is required, which is likely unavailable and requires costly and time-consuming screening experiments to obtain. As the majority of existing deep generative frameworks (see Sousa, et al 5 for a review of generative deep learning for targeted molecule design) still rely on learning from targetspecific libraries of binder compounds, they limit exploration beyond a fixed library of known and monolithic molecules, while 2/ 40 preventing generalization of the machine learning framework toward more novel targets. As a result, while some studies [6][7][8] that use deep generative models for target-specific inhibitor design have been experimentally validated, rarely have such models demonstrated sufficient versatility to be broadly deployable across dissimilar protein targets, without having access to detailed target-specific prior knowledge (e.g., target structure or binder library).…”

Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets

“…To our knowledge, this is the first validated demonstration of a single generative model enabling successful and efficient discovery of inhibitor molecules for two different target proteins, based only upon the protein sequence and without the prior knowledge of target-specific ligand binding data or target structure. Previous generative machine learning models that have been subject to experimental validation of de novo-designed molecules were primarily either trained or fine-tuned on a target-specific ligand library 6,7,[34][35][36][37][38] .…”