Beyond Size, Ionization State, and Lipophilicity: Influence of Molecular Topology on Absorption, Distribution, Metabolism, Excretion, and Toxicity for Druglike Compounds

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of a compound is dependent on physicochemical properties such as molecular size, lipophilicity, and ionization state. However, much less is known regarding the relationship between ADMET and the molecular topology. In this study two descriptors related to the molecular topology have been investigated, the fraction of the molecular framework (<i>f</i>_MF) and the fraction of sp³-hybridized carbon atoms (Fsp³). <i>f</i>_MF and Fsp³, together with standard physicochemical properties (molecular size, ionization state, and lipophilicity), were analyzed for a set of ADMET assays. It is shown that aqueous solubility, Caco-2 permeability, plasma protein binding, human ether-a-go-go-related potassium channel protein inhibition, and CYP3A4 (CYP = cytochrome P450) inhibition are influenced by the molecular topology. These findings are in most cases independent of the already well-established relationships between the properties and molecular size, lipophilicity, and ionization state

A new Era of Federal Prescrbed Fire: Defining Terminology and Properly Applying the Discretionary Function Exception

Additional file 1. Equivalence to REINFORCE. Proof that the method used can be described as a REINFORCE type algorithm

Beyond Size, Ionization State, and Lipophilicity: Influence of Molecular Topology on Absorption, Distribution, Metabolism, Excretion, and Toxicity for Druglike Compounds

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of a compound is dependent on physicochemical properties such as molecular size, lipophilicity, and ionization state. However, much less is known regarding the relationship between ADMET and the molecular topology. In this study two descriptors related to the molecular topology have been investigated, the fraction of the molecular framework (<i>f</i>_MF) and the fraction of sp³-hybridized carbon atoms (Fsp³). <i>f</i>_MF and Fsp³, together with standard physicochemical properties (molecular size, ionization state, and lipophilicity), were analyzed for a set of ADMET assays. It is shown that aqueous solubility, Caco-2 permeability, plasma protein binding, human ether-a-go-go-related potassium channel protein inhibition, and CYP3A4 (CYP = cytochrome P450) inhibition are influenced by the molecular topology. These findings are in most cases independent of the already well-established relationships between the properties and molecular size, lipophilicity, and ionization state

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project

Beyond the Scope of Free-Wilson Analysis. 2: Can Distance Encoded R‑Group Fingerprints Provide Interpretable Nonlinear Models?

In a recent study, we presented a novel quantitative-structure–activity-relationship (QSAR) approach, combining R-group signatures and nonlinear support-vector-machines (SVM), to build interpretable local models for congeneric compound sets. Here, we outline further refinements in the fingerprint scheme for the purpose of analyzing and visualizing structure–activity relationships (SAR). The concept of distance encoded R-group signature descriptors is introduced, and we explore the influence of different signature encoding schemes on both interpretability and predictive power of the SVM models using ten public data sets. The R-group and atomic gradients provide a way to interpret SVM models and enable detailed analysis of structure–activity relationships within substituent groups. We discuss applications of the method and show how it can be used to analyze nonadditive SAR and provide intuitive and powerful SAR visualizations

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery