ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening.

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components.

Discovery and Preclinical Characterization of BIIB129, a Covalent, Selective, and Brain-Penetrant BTK Inhibitor for the Treatment of Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton's tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 (), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening.

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds.

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein-ligand binding affinity predictions in early drug discovery programs.

cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation.

Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo target-specific molecular design. By implementing different keys and values for the multi-head attention conditional on a specified target, the proposed method can generate drug-like compounds both with and without a specific target.

Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective.

Absorption, distribution, metabolism, and excretion (ADME), which collectively define the concentration profile of a drug at the site of action, are of critical importance to the success of a drug candidate. Recent advances in machine learning algorithms and the availability of larger proprietary as well as public ADME data sets have generated renewed interest within the academic and pharmaceutical science communities in predicting pharmacokinetic and physicochemical endpoints in early drug discovery. In this study, we collected 120 internal prospective data sets over 20 months across six ADME in vitro endpoints: human and rat liver microsomal stability, MDR1-MDCK efflux ratio, solubility, and human and rat plasma protein binding.

Controlled sulfur-based engineering confers mouldability to phosphorothioate antisense oligonucleotides.

Phosphorothioates (PS) have proven their effectiveness in the area of therapeutic oligonucleotides with applications spanning from cancer treatment to neurodegenerative disorders. Initially, PS substitution was introduced for the antisense oligonucleotides (PS ASOs) because it confers an increased nuclease resistance meanwhile ameliorates cellular uptake and in-vivo bioavailability. Thus, PS oligonucleotides have been elevated to a fundamental asset in the realm of gene silencing therapeutic methodologies.

Critical Assessment of State-of-the-Art Ligand-Based Virtual Screening Methods.

The availability of large chemical libraries containing hundreds of millions to billions of diverse drug-like molecules combined with an almost unlimited amount of compute power to achieve scientific calculations has led investors and researchers to have a renewed interest in virtual screening (VS) methods to identify biologically active compounds. The number of in silico screening tools and software which employ the knowledge of the protein target or known bioactive ligands is increasing at a rapid pace, creating a crowded computational landscape where it has become difficult to assess the real advantages and disadvantages in terms of accuracy and efficiency of each individual VS technology. In the current work, we evaluate the performance of several state-of-the-art commercial software for 3D ligand-based VS against well-known 2D methods using an internally curated benchmarking data set.

Harnessing Preclinical Data as a Predictive Tool for Human Brain Tissue Targeting.

One of the objectives within the medicinal chemistry discipline is to design tissue targeting molecules. The objective of tissue specificity can be either to gain drug access to the compartment of interest (, the CNS) for Neuroscience targets or to restrict drug access to the CNS for all other therapeutic areas. Both neuroscience and non-neuroscience therapeutic areas have struggled to quantitatively estimate brain penetration or the lack thereof with compounds that are substrates of efflux transport proteins such as P-glycoprotein (P-gp) and breast cancer resistant protein (BCRP) that are key components of the blood-brain barrier (BBB).

PFRED: A computational platform for siRNA and antisense oligonucleotides design.

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency.

Photoaffinity Labeling and Quantitative Chemical Proteomics Identify LXRβ as the Functional Target of Enhancers of Astrocytic apoE.

Utilizing a phenotypic screen, we identified chemical matter that increased astrocytic apoE secretion in vitro. We designed a clickable photoaffinity probe based on a pyrrolidine lead compound and carried out probe-based quantitative chemical proteomics in human astrocytoma CCF-STTG1 cells to identify liver x receptor β (LXRβ) as the target. Binding of the small molecule ligand stabilized LXRβ, as shown by cellular thermal shift assay (CETSA).

DeltaDelta neural networks for lead optimization of small molecule potency.

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks.

A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues.

The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies.

Understanding allosteric interactions in hMLKL protein that modulate necroptosis and its inhibition.

Mixed Lineage Kinase domain-Like (MLKL), a key player in necroptosis, is a multi-domain protein with an N-terminal 4 helical bundle (4HB) and a pseudokinase domain (PsK) connected by brace helices. Phosphorylation of PsK domain of MLKL is a key step towards oligomerization of 4HB domain that causes cell death. Necrosulfonamide (NSA) binds to the 4HB domain of MLKL to inhibit necroptosis.

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network to generate, rather than search, diverse three-dimensional ligand shapes complementary to the pocket. Furthermore, we show that the generated molecule shapes can be decoded using a shape-captioning network into a sequence of SMILES enabling directly the structure-based de novo drug design.

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow.

The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns.

Author Correction: Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs.

The recent increase in the number of X-ray crystal structures of G-protein coupled receptors (GPCRs) has been enabling for structure-based drug design (SBDD) efforts. These structures have revealed that GPCRs are highly dynamic macromolecules whose function is dependent on their intrinsic flexibility. Unfortunately, the use of static structures to understand ligand binding can potentially be misleading, especially in systems with an inherently high degree of conformational flexibility.

Hybrid peptide-small molecule oxytocin analogs are potent and selective agonists of the oxytocin receptor.

Oxytocin (OT) is a peptide hormone agonist of the oxytocin receptor (OTR) that has been proposed as a therapeutic to treat a number of social and emotional disorders in addition to its current clinical use to induce labor and treat postpartum bleeding. OT is administered intravenously and intranasally rather than orally, in part because its low passive permeability causes low oral bioavailability. Non-peptidic OTR agonists have also been reported, but none with the exquisite potency of the peptide based agonists.

The Discovery of a Novel Phosphodiesterase (PDE) 4B-Preferring Radioligand for Positron Emission Tomography (PET) Imaging.

As part of our effort in identifying phosphodiesterase (PDE) 4B-preferring inhibitors for the treatment of central nervous system (CNS) disorders, we sought to identify a positron emission tomography (PET) ligand to enable target occupancy measurement in vivo. Through a systematic and cost-effective PET discovery process, involving expression level (B) and biodistribution determination, a PET-specific structure-activity relationship (SAR) effort, and specific binding assessment using a LC-MS/MS "cold tracer" method, we have identified 8 (PF-06445974) as a promising PET lead. Compound 8 has exquisite potency at PDE4B, good selectivity over PDE4D, excellent brain permeability, and a high level of specific binding in the "cold tracer" study.

Dihedral Angle-Based Sampling of Natural Product Polyketide Conformations: Application to Permeability Prediction.

Macrocycles pose challenges for computer-aided drug design due to their conformational complexity. One fundamental challenge is identifying all low-energy conformations of the macrocyclic ring, which is important for modeling target binding, passive membrane permeation, and other conformation-dependent properties. Macrocyclic polyketides are medically and biologically important natural products characterized by structural and functional diversity.

Systematic N-methylation of oxytocin: Impact on pharmacology and intramolecular hydrogen bonding network.

Oxytocin (OT) is a peptide hormone agonist of the OT receptor (OTR) that plays an important role in social behaviors such as pair bonding, maternal bonding and trust. The pharmaceutical development of OT as an oral peptide therapeutic has been hindered historically by its unfavorable physicochemical properties, including molecular weight, polarity and number of hydrogen bond donors, which determines poor cell permeability. Here we describe the first systematic study of single and multiple N-methylations of OT and their effect on physicochemical properties as well as potency at the OT receptor.

A Novel Binding Mode Reveals Two Distinct Classes of NMDA Receptor GluN2B-selective Antagonists.

N-methyl-d-aspartate receptors (NMDARs) are glutamate-gated ion channels that play key roles in brain physiology and pathology. Because numerous pathologic conditions involve NMDAR overactivation, subunit-selective antagonists hold strong therapeutic potential, although clinical successes remain limited. Among the most promising NMDAR-targeting drugs are allosteric inhibitors of GluN2B-containing receptors.

High throughput method for the indirect detection of intramolecular hydrogen bonding.

A supercritical fluid chromatography method was developed for the detection of intramolecular hydrogen bonds in pharmaceutically relevant molecules. The identification of compounds likely to form intramolecular hydrogen bonds is an important drug design consideration given the correlation of intramolecular hydrogen bonding with increased membrane permeability. The technique described here correlates chromatographic retention with the exposed polarity of a molecule.