[1,2,4]Triazolo[1,5-a]pyrimidine Phosphodiesterase 2A Inhibitors: Structure and Free-Energy Perturbation-Guided Exploration

Progress in the study of anti-Alzheimer's disease activity of pyrimidine-containing bioactive molecules

Pyrimidines are aromatic, heterocyclic organic compounds characterized by a six-membered ring that contains four carbon atoms and two nitrogen atoms. They have been reported to exhibit a variety of biological activities such as antifungal, antiviral, and anti-Parkinsonian effects. Recently, there has been an increased focus on their potential anti-Alzheimer's properties. Several pyrimidine-based drugs and their analogs are currently undergoing various phases of clinical trials, indicating pyrimidine as a promising chemical structure for drug development. Notably, modifications to the pyrimidine structure significantly influence their activity against Alzheimer's disease. For instance, the introduction of heteroatoms into the pyrimidine ring or alternations in the length of the linkage region have been shown to enhance therapeutic efficacy. This review provides a comprehensive overview of pyrimidine derivatives as potential therapeutics for Alzheimer's disease, with a focus on structure-activity relationship (SAR) studies, design strategies, and binding mechanisms. These insights could pave the way for the development of more effective anti-Alzheimer's medications.

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Discovery of (R)-4-(8-methoxy-2-methyl-1-(1-phenylethy)-1H-imidazo[4,5-c]quinnolin-7-yl)-3,5-dimethylisoxazole as a potent and selective BET inhibitor for treatment of acute myeloid leukemia (AML) guided by FEP calculation

The functions of the bromodomain and extra terminal (BET) family of proteins have been proved to be involved in various diseases, particularly the acute myeloid leukemia (AML). In this work, guided by free energy perturbation (FEP) calculation, a methyl group was selected to be attached to the 1H-imidazo[4,5-c]quinoline skeleton, and a series of congeneric compounds were synthesized. Among them, compound 10 demonstrated outstanding activity against BRD4 BD1 with an IC50 value of 1.9 nM and exhibited remarkable antiproliferative effects against MV4-11 cells. The X-ray cocrystal structure proved that 10 occupied the acetylated lysine (KAc) binding cavity and the WPF shelf of BRD4 BD1. Additionally, 10 displayed high selectivity towards BET family members, effectively inhibiting the growth of AML cells, promoting apoptosis, and arresting the cell cycle at the G0/G1 phase. Further mechanistic studies demonstrated that compound 10 could suppress the expression of c-Myc and CDK6 while enhancing the expression of P21, PARP, and cleaved PARP. Moreover, 10 exhibited remarkable pharmacokinetic properties and significant antitumor efficacy in vivo. Therefore, compound 10 may represent a new, potent and selective BET bromodomain inhibitor for the development of therapeutics to treat AML.

Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities

Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

The maximal and current accuracy of rigorous protein-ligand binding free energy calculations

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.

FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning

Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.

Review of triazole scaffolds for treatment and diagnosis of Alzheimer's disease

Triazole scaffolds, a series of 5-membered heterocycles, are well known for their high efficacy, low toxicity, and superior pharmacokinetics. Alzheimer's disease (AD) is the first neurodegenerative disorder with complex pathological mechanisms. Triazole, as an aromatic group with three nitrogen atoms, forms polar and non-polar interactions with diverse key residues in the receptor-ligand binding procedure, and has been widely used in the molecular design in the development of anti-AD agents. Moreover, considering the simple synthesis approaches, triazole scaffolds are commonly used to link two pharmacodynamic groups in one chemical molecule, forming multi-target directed ligands (MTDLs). Furthermore, the click reaction between azide- and cyano-modified enzyme and ligand provides feasibility for the new modulator discovery, compound tissue distribution evaluation, enzyme localization, and pharmacological mechanism study, promoting the diagnosis of AD course.

Characterization of 2 Novel Phosphodiesterase 2 Inhibitors Hcyb1 and PF-05180999 on Depression- and Anxiety-Like Behavior

BACKGROUND

Phosphodiesterase 2A (PDE2A) represents a novel target for new therapies addressing psychiatric disorders. To date, the development of PDE2A inhibitors suitable for human clinical evaluation has been hampered by the poor brain accessibility and metabolic stability of the available compounds.

METHODS

Corticosterone (CORT)-induced neuronal cell lesion and restraint stress mouse model were used to measure the neuroprotective effect in cells and antidepressant-like behavior in mice.

RESULTS

The cell-based assay showed that both Hcyb1 and PF were potent in protecting cells against stress hormone CORT insults by stimulating cAMP and cGMP signaling in hippocampal cells (HT-22). Administration of both compounds before treatment of CORT to cells increased cAMP/cGMP, VASP phosphorylation at Ser239 and Ser157, cAMP response element binding protein phosphorylation at Ser133, and brain derived neurotrophic factor BDNF expression. Further in vivo study showed that both Hcyb1 and PF displayed -antidepressant- and anxiolytic-like effects against restraint stress as indicated by reduced immobility time in the forced swimming and tail suspension tasks as well as increased open arm entries and time spent in open arms and holes visit in elevated plus maze and hole-board tests, respectively. The biochemical study confirmed that these antidepressant- and anxiolytic-like effects of Hcyb1 and PF were related to cAMP and cGMP signaling in the hippocampus.

CONCLUSIONS

The results extend the previous studies and validate that PDE2A is a tractable target for drug development in the treatment of emotional disorders such as depression and anxiety.

From computer-aided drug discovery to computer-driven drug discovery

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In the last decade however, there have been dramatic advances in the field, including (1) development of physics-based computational approaches to accurately predict a broad variety of endpoints from potency to solubility, (2) improvements in artificial intelligence and deep learning methods and (3) dramatic increases in computational power with the advent of GPUs and cloud computing, resulting in the ability to explore and accurately profile vast amounts of drug-like chemical space in silico. There have also been simultaneous advancements in structural biology such as cryogenic electron microscopy (cryo-EM) and computational protein-structure prediction, allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. The convergence of these breakthroughs has positioned structurally-enabled computational methods to be a driving force behind the discovery of novel small molecule therapeutics. This review will give a broad overview of the synergies in recent advances in the fields of computational chemistry, machine learning and structural biology, in particular in the areas of hit identification, hit-to-lead, and lead optimization.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Triazolopyrimidine Nuclei: Privileged Scaffolds for Developing Antiviral Agents with a Proper Pharmacokinetic Profile

Viruses are a continuing threat to global health. The lack or limited therapeutic armamentarium against some viral infections and increasing drug resistance issues make the search for new antiviral agents urgent. In recent years, a growing literature highlighted the use of triazolopyrimidine (TZP) heterocycles in the development of antiviral agents, with numerous compounds that showed potent antiviral activities against different RNA and DNA viruses. TZP core represents a privileged scaffold for achieving biologically active molecules, thanks to: i) the synthetic feasibility that allows to variously functionalize TZPs in the different positions of the nucleus, ii) the ability of TZP core to establish multiple interactions with the molecular target, and iii) its favorable pharmacokinetic properties. In the present review, after mentioning selected examples of TZP-based compounds with varied biological activities, we will focus on those antivirals that appeared in the literature in the last 10 years. Approaches used for their identification, the hit-to-lead studies, and the emerged structure-activity relationship will be described. A mention of the synthetic methodologies to prepare TZP nuclei will also be given. In addition, their mechanism of action, the binding mode within the biological target, and pharmacokinetic properties will be analyzed, highlighting the strengths and weaknesses of compounds based on the TZP scaffold, which is increasingly used in medicinal chemistry.