A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling.

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

Exploring the Structural Space of the Galectin-1-Ligand Interaction.

Galectin-1 is a tumor-associated protein recognizing the Galß1-4GlcNAc motif of cell-surface glycoconjugates. Herein, we report the stepwise expansion of a multifunctional natural scaffold based on N-acetyllactosamine (LacNAc). We obtained a LacNAc mimetic equipped with an alkynyl function on the 3'-hydroxy group of the disaccharide facing towards a binding pocket adjacent to the carbohydrate-recognition domain. It served as an anchor motif for further expansion by the Sharpless-Huisgen-Meldal reaction, which resulted in ligands with a binding mode almost identical to that of the natural carbohydrate template. X-ray crystallography provided a structural understanding of the galectin-1-ligand interactions. The results of this study enable the development of bespoke ligands for members of the galectin target family.

Machine learning estimates of natural product conformational energies.

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures.

Multidimensional de†novo design reveals 5-HT2B receptor-selective ligands.

We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.

Multi-objective molecular de novo design by adaptive fragment prioritization.

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.

Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.

Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates.

Selective and brain-penetrant HCN1 inhibitors reveal links between synaptic integration, cortical function, and working memory.

Hyperpolarization-activated and cyclic-nucleotide-gated 1 (HCN1) ion channels are proposed to be critical for cognitive function through regulation of synaptic integration. However, resolving the precise role of HCN1 in neurophysiology and exploiting its therapeutic potential has been hampered by minimally selective antagonists with poor potency and limited in vivo efficiency. Using automated electrophysiology in a small-molecule library screen and chemical optimization, we identified a primary carboxamide series of potent and selective HCN1 inhibitors with a distinct mode of action. In cognition-relevant brain circuits, selective inhibition of native HCN1 produced on-target effects, including enhanced excitatory postsynaptic potential summation, while administration of a selective HCN1 inhibitor to rats recovered decrement working memory. Unlike prior non-selective HCN antagonists, selective HCN1 inhibition did not alter cardiac physiology in human atrial cardiomyocytes or in rats. Collectively, selective HCN1 inhibitors described herein unmask HCN1 as a potential target for the treatment of cognitive dysfunction in brain disorders.

Adaptive peptide design.

Computer algorithms help in the identification and optimization of peptides with desired structure and function. We provide an overview of the current focus of our research group in this field, highlighting innovative methods for peptide representation and de novo peptide generation. Our evolutionary molecular design cycle contains structure-activity relationship modeling by machine-learning methods, virtual peptide generation, activity prediction, peptide syntheses, as well as biophysical and biochemical activity determination. Such interplay between computer-assisted peptide generation and scoring with real laboratory experiments enables rapid feedback throughout the design cycle so that adaptive optimization can take place. Selected practical applications are reviewed including the design of new immunomodulatory MHC-I binding peptides and antimicrobial peptides.

Drugs by numbers: reaction-driven de novo design of potent and selective anticancer leads.

A potent and selective inhibitor of the anticancer target Polo-like kinase 1 was found by computer-based molecular design. This type II kinase inhibitor was synthesized as suggested by the design software DOGS and exhibited significant antiproliferative effects against HeLa cells without affecting nontransformed cells. The study provides a proof-of-concept for reaction-based de novo design as a leading tool for drug discovery.

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces.

In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features. We consider four GNN variants - Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint (AttentiveFP). So far deep learning models have been primarily benchmarked using lower-bar traditional models solely based on fingerprints, while more realistic benchmarks employing fingerprints, whole-molecule descriptors and predictions from other related endpoints (e. g., LogD7.4) appear to be scarce for industrial ADME datasets. In addition to time-split test sets based on Genentech data, this study benefits from the availability of measurements from an external chemical space (Roche data). We identify GAT as a promising approach to implementing deep learning models. While all the deep learning models significantly outperform lower-bar benchmark traditional models solely based on fingerprints, only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models. Finally, the accuracy of in vitro assays from different laboratories predicting the same experimental endpoints appears to be comparable with the accuracy of GAT single-task models, suggesting that most of the observed error from the models is a function of the experimental error propagation.

Rationalizing tight ligand binding through cooperative interaction networks.

Small modifications of the molecular structure of a ligand sometimes cause strong gains in binding affinity to a protein target, rendering a weakly active chemical series suddenly attractive for further optimization. Our goal in this study is to better rationalize and predict the occurrence of such interaction hot-spots in receptor binding sites. To this end, we introduce two new concepts into the computational description of molecular recognition. First, we take a broader view of noncovalent interactions and describe protein-ligand binding with a comprehensive set of favorable and unfavorable contact types, including for example halogen bonding and orthogonal multipolar interactions. Second, we go beyond the commonly used pairwise additive treatment of atomic interactions and use a small world network approach to describe how interactions are modulated by their environment. This approach allows us to capture local cooperativity effects and considerably improves the performance of a newly derived empirical scoring function, ScorpionScore. More importantly, however, we demonstrate how an intuitive visualization of key intermolecular interactions, interaction networks, and binding hot-spots supports the identification and rationalization of tight ligand binding.

Discovery of RO7185876, a Highly Potent γ-Secretase Modulator (GSM) as a Potential Treatment for Alzheimer's Disease.

γ-Secretase (GS) is a key target for the potential treatment of Alzheimer's disease. While inhibiting GS led to serious side effects, its modulation holds a lot of potential to deliver a safe treatment. Herein, we report the discovery of a potent and selective gamma secretase modulator (GSM) (S)-3 (RO7185876), belonging to a novel chemical class, the triazolo-azepines. This compound demonstrates an excellent in vitro and in vivo DMPK profile. Furthermore, based on its in vivo efficacy in a pharmacodynamic mouse model and the outcome of the dose range finding (DRF) toxicological studies in two species, this compound was selected to undergo entry in human enabling studies (e.g., GLP toxicology and scale up activities).

Voyages to the (un)known: adaptive design of bioactive compounds.

De novo drug design has emerged as a valuable concept for the rapid identification of lead structure candidates. In particular, fragment-based molecular assembly methods have been successfully employed for the automated design of screening compounds. Here, we review the current status of these approaches, with an emphasis on adaptive techniques that can be used to artificially evolve novel bioactive molecules. Evolutionary algorithms (EAs) and particle swarm optimization (PSO) are presented as preferred techniques for iterative virtual synthesis and testing. By the inclusion of straightforward synthesis rules, druglike compounds can be obtained. Evolving compound libraries are particularly suited for hit and lead finding in situations where resources are limited and the complete testing of a large screening compound collection is prohibitive.

Understanding Molecular Drivers of Melanin Binding To Support Rational Design of Small Molecule Ophthalmic Drugs.

Binding of drugs to ocular melanin is a prominent biological phenomenon that affects the local pharmacokinetics and pharmacodynamics in the eye. In this work, we report on the development of in vitro and in silico tools for an early assessment and prediction of melanin binding properties of small molecules. A robust high-throughput assay has been established to study the binding of large sets of compounds to melanin. The extremely randomized trees approach was used to develop an in silico model able to predict the extent of melanin binding from the molecular properties of the compounds. After the last iteration of the model, strong melanin binders could prospectively be identified with 91% accuracy. On the basis of in vitro data generated for approximately 3400 chemically diverse drug-like small molecules, pronounced correlations were observed between the extent of melanin binding and the basicity, lipophilicity, and aromaticity of the compounds.

Discovery of Risdiplam, a Selective Survival of Motor Neuron-2 ( SMN2) Gene Splicing Modifier for the Treatment of Spinal Muscular Atrophy (SMA).

SMA is an inherited disease that leads to loss of motor function and ambulation and a reduced life expectancy. We have been working to develop orally administrated, systemically distributed small molecules to increase levels of functional SMN protein. Compound 2 was the first SMN2 splicing modifier tested in clinical trials in healthy volunteers and SMA patients. It was safe and well tolerated and increased SMN protein levels up to 2-fold in patients. Nevertheless, its development was stopped as a precautionary measure because retinal toxicity was observed in cynomolgus monkeys after chronic daily oral dosing (39 weeks) at exposures in excess of those investigated in patients. Herein, we describe the discovery of 1 (risdiplam, RG7916, RO7034067) that focused on thorough pharmacology, DMPK and safety characterization and optimization. This compound is undergoing pivotal clinical trials and is a promising medicine for the treatment of patients in all ages and stages with SMA.

Discovery of a Novel Class of Survival Motor Neuron 2 Splicing Modifiers for the Treatment of Spinal Muscular Atrophy.

Spinal muscular atrophy (SMA) is caused by mutation or deletion of the survival motor neuron 1 (SMN1) gene, resulting in low levels of functional SMN protein. We have reported recently the identification of small molecules (coumarins, iso-coumarins and pyrido-pyrimidinones) that modify the alternative splicing of SMN2, a paralogous gene to SMN1, restoring the survival motor neuron (SMN) protein level in mouse models of SMA. Herein, we report our efforts to identify a novel chemotype as one strategy to potentially circumvent safety concerns from earlier derivatives such as in vitro phototoxicity and in vitro mutagenicity associated with compounds 1 and 2 or the in vivo retinal findings observed in a long-term chronic tox study with 3 at high exposures only. Optimized representative compounds modify the alternative splicing of SMN2, increase the production of full length SMN2 mRNA, and therefore levels of full length SMN protein upon oral administration in two mouse models of SMA.