Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

1D and 2D NMR for KRAS:Ligand Binding.

Fragment-based screening by ligand-observed 1D NMR and binding interface mapping by protein-observed 2D NMR are popular methods used in drug discovery. These methods allow researchers to detect compound binding over a wide range of affinities and offer a simultaneous assessment of solubility, purity, and chemical formula accuracy of the target compounds and the 15N-labeled protein when examined by 1D and 2D NMR, respectively. These methods can be applied for screening fragment binding to the active (GMPPNP-bound) and inactive (GDP-bound) states of oncogenic KRAS mutants.

KRAS4b:RAF-1 Homogenous Time-Resolved Fluorescence Resonance Energy Transfer Assay for Drug Discovery.

Homogenous time-resolved FRET (HTRF) assays have become one of the most popular tools for pharmaceutical drug screening efforts over the last two decades. Large Stokes shifts and long fluorescent lifetimes of lanthanide chelates lead to robust signal to noise, as well as decreased false positive rates compared to traditional assay techniques. In this chapter, we describe an HTRF protein-protein interaction (PPI) assay for the KRAS4b G-domain in the GppNHp-bound state and the RAF-1-RBD currently used for drug screens. Application of this assay contributes to the identification of lead compounds targeting the GTP-bound active state of K-RAS.

Stability-based approaches in chemoproteomics.

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene.

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling.

The accurate identification of drug-target affinity (DTA) is crucial for advancements in drug discovery and development. Many deep learning-based approaches have been devised to predict drug-target binding affinity accurately, exhibiting notable improvements in performance. However, the existing prediction methods often fall short of capturing the global features of proteins. In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming other baseline methods. The evaluation results on the KIBA dataset reveal that our model achieves the lowest mean square error (MSE) of 0.125, representing a 0.6% reduction compared to the lowest-performing baseline method. Furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (CNN) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of DTA prediction.

GLDM: hit molecule generation with constrained graph latent diffusion model.

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.

Discovery and Structure-Activity Relationship of a Ryanodine Receptor 2 Inhibitor.

Ryanodine receptor 2 (RyR2) is a large Ca2+-release channel in the sarcoplasmic reticulum (SR) of cardiac muscle cells. It serves to release Ca2+ from the SR into the cytosol to initiate muscle contraction. RyR2 overactivation is associated with arrhythmogenic cardiac disease, but few specific inhibitors have been reported so far. Here, we identified an RyR2-selective inhibitor 1 from the chemical compound library and synthesized it from glycolic acid. Synthesis of various derivatives to investigate the structure-activity relationship of each substructure afforded another two RyR2-selective inhibitors 6 and 7, among which 6 was the most potent. Notably, compound 6 also inhibited Ca2+ release in cells expressing the RyR2 mutants R2474S, R4497C and K4750Q, which are associated with cardiac arrhythmias such as catecholaminergic polymorphic ventricular tachycardia (CPVT). This inhibitor is expected to be a useful tool for research on the structure and dynamics of RyR2, as well as a lead compound for the development of drug candidates to treat RyR2-related cardiac disease.

Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

Medium-Sized Ring Expansion Strategies: Enhancing Small-Molecule Library Development.

The construction of a small molecule library that includes compounds with medium-sized rings is increasingly essential in drug discovery. These compounds are essential for identifying novel therapeutic agents capable of targeting "undruggable" targets through high-throughput and high-content screening, given their structural complexity and diversity. However, synthesizing medium-sized rings presents notable challenges, particularly with direct cyclization methods, due to issues such as transannular strain and reduced degrees of freedom. This review presents an overview of current strategies in synthesizing medium-sized rings, emphasizing innovative approaches like ring-expansion reactions. It highlights the challenges of synthesis and the potential of these compounds to diversify the chemical space for drug discovery, underscoring the importance of medium-sized rings in developing new bioactive compounds.

Design, Synthesis, and Evaluation of B-(Trifluoromethyl)phenyl Phosphine-Borane Derivatives as Novel Progesterone Receptor Antagonists.

We previously revealed that phosphine-boranes can function as molecular frameworks for biofunctional molecules. In the present study, we exploited the diversity of available phosphines to design and synthesize a series of B-(trifluoromethyl)phenyl phosphine-borane derivatives as novel progesterone receptor (PR) antagonists. We revealed that the synthesized phosphine-borane derivatives exhibited LogP values in a predictable manner and that the P-H group in the phosphine-borane was almost nonpolar. Among the synthesized phosphine-boranes, which exhibited PR antagonistic activity, B-(4-trifluoromethyl)phenyl tricyclopropylphosphine-borane was the most potent with an IC50 value of 0.54 µM. A docking simulation indicated that the tricyclopropylphosphine moiety plays an important role in ligand-receptor interactions. These results support the idea that phosphine-boranes are versatile structural options in drug discovery, and the developed compounds are promising lead compounds for further structural development of next-generation PR antagonists.

Recent Advances in the Total Synthesis of Spirotryprostatin Alkaloids.

Spirotryprostatin alkaloids, a class of alkaloids with a unique spirocyclic indoledionepiperazine structure, were first extracted from the fermentation broth of Aspergillus fumigatus and have garnered significant attention in the fields of biology and pharmacology. The investigation into the pharmacological potential of this class of alkaloids has unveiled promising applications in drug discovery and development. Notably, certain spirotryprostatin alkaloids have demonstrated remarkable anti-cancer activity, positioning them as potential candidates for anti-tumor drug development. In recent years, organic synthetic chemists have dedicated efforts to devise efficient and viable strategies for the total synthesis of spirotryprostatin alkaloids, aiming to meet the demands within the pharmaceutical domain. The construction of the spiro-C atom within the spirotryprostatin scaffold and the chirality control at the spiro atomic center emerge as pivotal aspects in the synthesis of these compounds. This review categorically delineates the synthesis of spirotryprostatin alkaloids based on the formation mechanism of the spiro-C atom.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities.

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments.

Applications of Flow Cytometry in Drug Discovery and Translational Research.

Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

Structure-Guided Discovery and Preclinical Assessment of Novel (Thiophen-3-yl)aminopyrimidine Derivatives as Potent ERK1/2 Inhibitors.

The RAS-RAF-MEK-ERK signaling cascade is abnormally activated in various tumors, playing a crucial role in mediating tumor progression. As the key component at the terminal stage of this cascade, ERK1/2 emerges as a potential antitumor target and offers a promising therapeutic strategy for tumors harboring BRAF or RAS mutations. Here, we identified 36c with a (thiophen-3-yl)aminopyrimidine scaffold as a potent ERK1/2 inhibitor through structure-guided optimization for hit 18. In preclinical studies, 36c showed powerful ERK1/2 inhibitory activities (ERK1/2 IC50 = 0.11/0.08 nM) and potent antitumor efficacy both in vitro and in vivo against triple-negative breast cancer and colorectal cancer models harboring BRAF and RAS mutations. 36c could directly inhibit ERK1/2, significantly block the phosphorylation expression of their downstream substrates p90RSK and c-Myc, and induce cell apoptosis and incomplete autophagy-related cell death. Taken together, this work provides a promising ERK1/2 lead compound for multiple tumor-treatment drug discovery.

Discovery of Novel Aryl Triazolone Dihydropyridines (ATDPs) Targeting Highly Conserved Residue W229 as Promising HIV-1 NNRTIs.

NNRTI is an important component of the highly active antiretroviral therapy (HAART), but the rapid emergence of drug resistance and poor pharmacokinetics limited their clinical application. Herein, a series of novel aryl triazolone dihydropyridines (ATDPs) were designed by structure-guided design with the aim of improving drug resistance profiles and pharmacokinetic profiles. Compound 10n (EC50 = 0.009-17.7 µM) exhibited the most active potency, being superior to or comparable to that of doravirine (DOR) against the whole tested viral panel. Molecular docking was performed to clarify the reason for its higher resistance profiles. Moreover, 10n demonstrated excellent pharmacokinetic profile (T1/2 = 5.09 h, F = 108.96%) compared that of DOR (T1/2 = 4.4 h, F = 57%). Additionally, 10n was also verified to have no in vivo acute or subacute toxicity (LD50 > 2000 mg/kg), suggesting that 10n is worth further investigation as a novel oral NNRTIs for HIV-1 therapy.