A decade of fragment-based drug design: strategic advances and lessons learned

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

Optimizing NMR fragment-based drug screening for membrane protein targets

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Development of SV2A Ligands for Epilepsy Treatment: A Review of Levetiracetam, Brivaracetam, and Padsevonil

Epilepsy is a common neurological disorder that is primarily treated with antiseizure medications (ASMs). Although dozens of ASMs are available in the clinic, approximately 30% of epileptic patients have medically refractory seizures; other limitations in most traditional ASMs include poor tolerability and drug-drug interactions. Therefore, there is an urgent need to develop alternative ASMs. Levetiracetam (LEV) is a first-line ASM that is well tolerated, has promising efficacy, and has little drug-drug interaction. Although it is widely accepted that LEV acts through a unique therapeutic target synaptic vesicle protein (SV) 2A, the molecular basis of its action remains unknown. Even so, the next-generation SV2A ligands against epilepsy based on the structure of LEV have achieved clinical success. This review highlights the research and development (R&D) process of LEV and its analogs, brivaracetam and padsevonil, to provide ideas and experience for the R&D of novel ASMs.

Molecular hybridization: a powerful tool for multitarget drug discovery

INTRODUCTION

The current drug discovery paradigm of 'one drug, multiple targets' has gained attention from both the academic medicinal chemistry community and the pharmaceutical industry. This is in response to the urgent need for effective agents to treat multifactorial chronic diseases. The molecular hybridization strategy is a useful tool that has been widely explored, particularly in the last two decades, for the design of multi-target drugs.

AREAS COVERED

This review examines the current state of molecular hybridization in guiding the discovery of multitarget small molecules. The article discusses the design strategies and target selection for a multitarget polypharmacology approach to treat various diseases, including cancer, Alzheimer's disease, cardiac arrhythmia, endometriosis, and inflammatory diseases.

EXPERT OPINION

Although the examples discussed highlight the importance of molecular hybridization for the discovery of multitarget bioactive compounds, it is notorious that the literature has focused on specific classes of targets. This may be due to a deep understanding of the pharmacophore features required for target binding, making targets such as histone deacetylases and cholinesterases frequent starting points. However, it is important to encourage the scientific community to explore diverse combinations of targets using the molecular hybridization strategy.

Particle fracture regimes from impact simulations

We introduce an approach to particle breakage, wherein the particle is modeled as an aggregate of polyhedral cells with their common surfaces governed by the Griffith criterion of fracture. This model is implemented within a discrete element code to simulate and analyze the breakage behavior of a single particle impacting a rigid plane. We find that fracture dynamics involves three distinct regimes as a function of the normalized impact energy ω. At low values of ω, the particle undergoes elastic rebound and no cracks occur inside the particle. In the intermediate range, the particle is damaged by nucleation and propagation of cracks, and the effective restitution coefficient declines without breakup of the particle. Finally, for values of ω beyond a well-defined threshold, the particle breaks into fragments and the restitution coefficient increases with ω due to kinetic energy carried away by the fragments. We show that particle damage, restitution coefficient, and fracture efficiency (the amount of energy input consumed for particle fracture) collapse well as a function of dimensionless scaling parameters. Our data are also sufficiently accurate to scale fragment size and shape distributions. It is found that fragment masses (volumes) follow a power-law distribution with an exponent decreasing with fracture energy. Interestingly, the average elongation and flatness of fragments are very close to those observed in experiments and lunar samples at the optimal fracture efficiency.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Molecular fragmentation as a crucial step in the AI-based drug development pathway

The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

On-DNA hydroalkylation of N-vinyl heterocycles via photoinduced EDA-complex activation

The emergence of DNA-encoded library (DEL) technology has provided a considerable advantage to the pharmaceutical industry in the pursuit of discovering novel therapeutic candidates for their drug development initiatives. This combinatorial technique not only offers a more economical, spatially efficient, and time-saving alternative to the existing ligand discovery methods, but also enables the exploration of additional chemical space by utilizing novel DNA-compatible synthetic transformations to leverage multifunctional building blocks from readily available substructures. In this report, a decarboxylative-based hydroalkylation of DNA-conjugated N-vinyl heterocycles enabled by single-electron transfer (SET) and subsequent hydrogen atom transfer through electron-donor/electron-acceptor (EDA) complex activation is detailed. The simplicity and robustness of this method permits inclusion of a broad array of alkyl radical precursors and DNA-tethered nitrogenous heterocyles to generate medicinally relevant substituted heterocycles with pendant functional groups. Moreover, a successful telescoped route provides the opportunity to access a broad range of intricate structural scaffolds by employing basic carboxylic acid feedstocks.

Expanding Bioactive Fragment Space with the Generated Database GDB-13s

Identifying innovative fragments for drug design can help medicinal chemistry address new targets and overcome the limitations of the classical molecular series. By deconstructing molecules into ring fragments (RFs, consisting of ring atoms plus ring-adjacent atoms) and acyclic fragments (AFs, consisting of only acyclic atoms), we find that public databases of molecules (i.e., ZINC and PubChem) and natural products (i.e., COCONUT) contain mostly RFs and AFs of up to 13 atoms. We also find that many RFs and AFs are enriched in bioactive vs inactive compounds from ChEMBL. We then analyze the generated database GDB-13s, which enumerates 99 million possible molecules of up to 13 atoms, for RFs and AFs resembling ChEMBL bioactive RFs and AFs. This analysis reveals a large number of novel RFs and AFs that are structurally simple, have favorable synthetic accessibility scores, and represent opportunities for synthetic chemistry to contribute to drug innovation in the context of fragment-based drug discovery.

SILVR: Guided Diffusion for Molecule Generation

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

A Benchmark Study of Protein-Fragment Complex Structure Calculations with NMR2

Protein-fragment complex structures are particularly sought after in medicinal chemistry to rationally design lead molecules. These structures are usually derived using X-ray crystallography, but the failure rate is non-neglectable. NMR is a possible alternative for the calculation of weakly interacting complexes. Nevertheless, the time-consuming protein signal assignment step remains a barrier to its routine application. NMR Molecular Replacement (NMR2) is a versatile and rapid method that enables the elucidation of a protein-ligand complex structure. It has been successfully applied to peptides, drug-like molecules, and more recently to fragments. Due to the small size of the fragments, ca < 300 Da, solving the structures of the protein-fragment complexes is particularly challenging. Here, we present the expected performances of NMR2 when applied to protein-fragment complexes. The NMR2 approach has been benchmarked with the SERAPhic fragment library to identify the technical challenges in protein-fragment NMR structure calculation. A straightforward strategy is proposed to increase the method's success rate further. The presented work confirms that NMR2 is an alternative method to X-ray crystallography for solving protein-fragment complex structures.

Recent research of BTK inhibitors: Methods of structural design, pharmacological activities, manmade derivatives and structure-activity relationship

Protein kinases constitute the largest group within the kinase family, and mutations and translocations of protein kinases due to genetic alterations are intimately linked to the pathogenesis of numerous diseases. Bruton's tyrosine kinase (BTK) is a member of the protein kinases and plays a pivotal role in the development and function of B cells. BTK belongs to the tyrosine TEC family. The aberrant activation of BTK is closely associated with the pathogenesis of B-cell lymphoma. Consequently, BTK has always been a critical target for treating hematological malignancies. To date, two generations of small-molecule covalent irreversible BTK inhibitors have been employed to treat malignant B-cell tumors, and have exhibited clinical efficacy in hitherto refractory diseases. However, these drugs are covalent BTK inhibitors, which inevitably lead to drug resistance after prolonged use, resulting in poor tolerance in patients. The third-generation non-covalent BTK inhibitor Pirtobrutinib has obtained approval for marketing in the United States, thereby circumventing drug resistance caused by C481 mutation. Currently, enhancing safety and tolerance constitutes the primary issue in developing novel BTK inhibitors. This article systematically summarizes recently discovered covalent and non-covalent BTK inhibitors and classifies them according to their structures. This article also provides a detailed discussion of binding modes, structural features, pharmacological activities, advantages and limitations of typical compounds within each structure type, providing valuable references and insights for developing safer, more effective and more targeted BTK inhibitors in future studies.

Identification of 3, 4-dihydroxy complexes as potential antiviral via DFT, molecular docking, molecular dynamics and MM/PBSA against rabies and dengue receptors

The quest to identify antiviral drug candidates for dengue and rabies viral diseases is a great challenge for the researchers. While different research is being conducted on the repurposed drugs against these two viruses, no drug compound has gained success in treating them. Therefore, in this study, 3, 4-dihydroxy complexes have been virtually designed to investigate their antiviral properties and analyze their efficiency in interaction with the concerned viral diseases. DFT calculations are carried out to study the electronic and thermodynamic properties to understand the stability and reactivity of the reported compounds. These compounds were subjected to molecular docking studies to understand the binding interactions with NS5 Dengue virus mRNA 2'-O-methyltransferase and phosphoprotein C-terminal domain of Rabies virus. MD simulation, hydrogen bond analysis, and MM/PBSA were performed at 100 ns to support the obtained docking results.Communicated by Ramaswamy H. Sarma.

An equivariant generative framework for molecular graph-structure Co-design

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Two-Step Validation Approach for Tools To Study the DNA Repair Enzyme SNM1A

We report a two-step validation approach to evaluate the suitability of metal-binding groups for targeting DNA damage-repair metalloenzymes using model enzyme SNM1A. A fragment-based screening approach was first used to identify metal-binding fragments suitable for targeting the enzyme. Effective fragments were then incorporated into oligonucleotides using the copper-catalysed azide-alkyne cycloaddition reaction. These modified oligonucleotides were recognised by SNM1A at >1000-fold lower concentrations than their fragment counterparts. The exonuclease SNM1A is a key enzyme involved in the repair of interstrand crosslinks, a highly cytotoxic form of DNA damage. However, SNM1A and other enzymes of this class are poorly understood, as there is a lack of tools available to facilitate their study. Our novel approach of incorporating functional fragments into oligonucleotides is broadly applicable to generating modified oligonucleotide structures with high affinity for DNA damage-repair enzymes.

Exploring the structure, function of thiamine pyrophosphate riboswitch, and designing small molecules for antibacterial activity

During the last decade, riboswitches emerged as new small-molecule sensing RNA in bacteria. Thiamine pyrophosphate (TPP) riboswitch is widely distributed and occurs in plants, bacteria, fungi, and archaea. Extensive biochemical, structural, and genetic studies have been carried out to elucidate the recognition mechanism of TPP riboswitches. However, a comprehensive report summarizing all information on recognition principles and newly designed ligands for TPP riboswitch is scarce in the literature. This review gives a comprehensive understanding of the TPP riboswitch's structure, mechanism, and methods applied to design ligands for the TPP riboswitch. The ligand-bound TPP riboswitch was studied with various experimental and theoretical techniques to elucidate the conformational dynamics. The mutation studies shed light on the significance of pyrimidine sensing helix for the binding of ligands. Further, the structure-activity relationship study and fragment-based approach lead to the development of ligands with Kd values at the sub-micromolar level. However, there is a need to design more potent inhibitors for TPP riboswitch for therapeutic applications. The recent advancements in ligand design highlight the TPP riboswitch as a promising target for developing new antibiotics. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Riboswitches Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Aminopyridone-linked benzimidazoles: a fragment-based drug design for the development of CDK9 inhibitors

Aim: A fragment-based design and synthesis of three novel series of aminopyridone-linked benzimidazoles as potential anticancer candidates with significant CDK9 inhibition was implemented. Materials & methods: All synthesized compounds were submitted to National Cancer Institute, 60 cell lines and seven-dose cytotoxicity toward three cancer cells. Results: Compounds 2, 4a, 4c, 4d, 6a and 8a exhibited significant cytotoxicity and selectivity with IC50 range of 7.61-57.75 μM. Regarding the mechanism either in vitro or in silico, 4a, 6a and 8a displayed potent CDK9 inhibition with IC50 value of 0.424-8.461 μM. Compound 6a arrested the cell cycle at S phase and induced apoptosis in MCF-7 cells. Conclusion: Compound 6a is a promising CDK9 inhibitor that warrants additional research for cancer treatment.

G2Retro as a two-step graph generative models for retrosynthesis prediction

Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper,we develop a generative framework G²Retro for one-step retrosynthesis prediction. G²Retro imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. G²Retro defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, G²Retro considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that G²Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Halo Library, a Tool for Rapid Identification of Ligand Binding Sites on Proteins Using Crystallographic Fragment Screening

X-ray crystallographic fragment screening (XCFS) uses fragment-sized molecules (∼60 to 300 Da) to access binding sites on proteins that may be inaccessible to larger drug-like molecules (>300 Da). Previous studies have shown that fragments containing halogen atoms bind more often to proteins than non-halogenated fragments. Here, we designed the Halo Library containing 46 halogenated fragments (including the "universal fragment" 4-bromopyrazole), a majority of which have been reported to bind to or inhibit one or more targets. The library was screened against the crystals of HIV-1 reverse transcriptase with the drug rilpivirine, yielding an overall hit rate of 26%. Two new binding sites were discovered, and several hot spots were identified. This small library may thus provide a convenient tool for rapidly assessing the feasibility of a target for XCFS, mapping hot spots and cryptic sites, as well as finding fragment binders that can be useful for developing drug leads.

Acyldepsipeptide Antibiotics and a Bioactive Fragment Thereof Differentially Perturb Mycobacterium tuberculosis ClpXP1P2 Activity in Vitro

Proteolytic complexes in Mycobacterium tuberculosis (Mtb), the deadliest bacterial pathogen, are major foci in tuberculosis drug development programs. The Clp proteases, which are essential for Mtb viability, are high-priority targets. These proteases function through the collaboration of ClpP1P2, a barrel-shaped heteromeric peptidase, with associated ATP-dependent chaperones like ClpX and ClpC1 that recognize and unfold specific substrates in an ATP-dependent fashion. The critical interaction of the peptidase and its unfoldase partners is blocked by the competitive binding of acyldepsipeptide antibiotics (ADEPs) to the interfaces of the ClpP2 subunits. The resulting inhibition of Clp protease activity is lethal to Mtb. Here, we report the surprising discovery that a fragment of the ADEPs retains anti-Mtb activity yet stimulates rather than inhibits the ClpXP1P2-catalyzed degradation of proteins. Our data further suggest that the fragment stabilizes the ClpXP1P2 complex and binds ClpP1P2 in a fashion distinct from that of the intact ADEPs. A structure-activity relationship study of the bioactive fragment defines the pharmacophore and points the way toward the development of new drug leads for the treatment of tuberculosis.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit Generation

Hit generation is a crucial step in drug discovery that will determine the speed and chance of success of identifying drug candidates. Many strategies are now available to identify chemical starting points, or hits, and each biological target warrants a tailored approach. In this set of best practices, we detail the essential approaches for target centric hit generation and the opportunities and challenges they come with. We then provide guidance on how to validate hits to ensure medicinal chemistry is only performed on compounds and scaffolds that engage the target of interest and have the desired mode of action. Finally, we discuss the design of integrated hit generation strategies that combine several approaches to maximize the chance of identifying high quality starting points to ensure a successful drug discovery campaign.

Proteome-Wide Fragment-Based Ligand and Target Discovery

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i. e., ligandability) directly in native systems. In this review, we discuss the utility of such approaches, with a focus on the integration of chemoproteomic methods with fragment-based ligand discovery (FBLD), to facilitate the broad mapping of the ligandable proteome while also providing starting points for progression into lead chemical probes.

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning

Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a  Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from  a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A_2A receptor (A_2AAR) and compared with SMILES-based methods. The results show that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A_2AAR with given scaffolds.

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

The application of zebrafish patient-derived xenograft tumor models in the development of antitumor agents

The cost of antitumor drug development is enormous, yet the clinical outcomes are less than satisfactory. Therefore, it is of great importance to develop effective drug screening methods that enable accurate, rapid, and high-throughput discovery of lead compounds in the process of preclinical antitumor drug research. An effective solution is to use the patient-derived xenograft (PDX) tumor animal models, which are applicable for the elucidation of tumor pathogenesis and the preclinical testing of novel antitumor compounds. As a promising screening model organism, zebrafish has been widely applied in the construction of the PDX tumor model and the discovery of antineoplastic agents. Herein, we systematically survey the recent cutting-edge advances in zebrafish PDX models (zPDX) for studies of pathogenesis mechanisms and drug screening. In addition, the techniques used in the construction of zPDX are summarized. The advantages and limitations of the zPDX are also discussed in detail. Finally, the prospects of zPDX in drug discovery, translational medicine, and clinical precision medicine treatment are well presented.

The pathogenic mechanism of Mycobacterium tuberculosis: implication for new drug development

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a tenacious pathogen that has latently infected one third of the world's population. However, conventional TB treatment regimens are no longer sufficient to tackle the growing threat of drug resistance, stimulating the development of innovative anti-tuberculosis agents, with special emphasis on new protein targets. The Mtb genome encodes ~4000 predicted proteins, among which many enzymes participate in various cellular metabolisms. For example, more than 200 proteins are involved in fatty acid biosynthesis, which assists in the construction of the cell envelope, and is closely related to the pathogenesis and resistance of mycobacteria. Here we review several essential enzymes responsible for fatty acid and nucleotide biosynthesis, cellular metabolism of lipids or amino acids, energy utilization, and metal uptake. These include InhA, MmpL3, MmaA4, PcaA, CmaA1, CmaA2, isocitrate lyases (ICLs), pantothenate synthase (PS), Lysine-ε amino transferase (LAT), LeuD, IdeR, KatG, Rv1098c, and PyrG. In addition, we summarize the role of the transcriptional regulator PhoP which may regulate the expression of more than 110 genes, and the essential biosynthesis enzyme glutamine synthetase (GlnA1). All these enzymes are either validated drug targets or promising target candidates, with drugs targeting ICLs and LAT expected to solve the problem of persistent TB infection. To better understand how anti-tuberculosis drugs act on these proteins, their structures and the structure-based drug/inhibitor designs are discussed. Overall, this investigation should provide guidance and support for current and future pharmaceutical development efforts against mycobacterial pathogenesis.

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Fragment-Based Dynamic Combinatorial Chemistry for Identification of Selective α-Glucosidase Inhibitors

Efforts to combine advantages of fragment-based drug design (FBDD) and dynamic combinatorial chemistry (DCC) for the development of selective α-glucosidase inhibitors were described. Starting from 5 rationally designed fragments, two iterative dynamic combinatorial libraries (DCLs) comprising 29 acylhydrazone products were generated and screened using α-glucosidase and α-amylase as the templates. The optimal ligand identified showed substantial α-glucosidase inhibition with high selectivity over α-amylase as well as low cytotoxicity. Furthermore, inhibition type and detailed ligand/enzyme binding interactions were elucidated by the binding kinetic study and docking simulation, respectively.

The IQA Energy Partition in a Drug Design Setting: A Hepatitis C Virus RNA-Dependent RNA Polymerase (NS5B) Case Study

The interaction of the thumb site II of the NS5B protein of hepatitis C virus and a pair of drug candidates was studied using a topological energy decomposition method called interacting quantum atoms (IQA). The atomic energies were then processed by the relative energy gradient (REG) method, which extracts chemical insight by computation based on minimal assumptions. REG reveals the most important IQA energy contributions, by atom and energy type (electrostatics, sterics, and exchange–correlation), that are responsible for the behaviour of the whole system, systematically from a short-range ligand–pocket interaction until a distance of approximately 22 Å. The degree of covalency in various key interatomic interactions can be quantified. No exchange–correlation contribution is responsible for the changes in the energy profile of both pocket–ligand systems investigated in the ligand–pocket distances equal to or greater than that of the global minimum. Regarding the hydrogen bonds in the system, a “neighbour effect” was observed thanks to the REG method, which states that a carbon atom would rather not have its covalent neighbour oxygen form a hydrogen bond. The combination of IQA and REG enables the automatic identification of the pharmacophore in the ligands. The coarser Interacting Quantum Fragments (IQF) enables the determination of which amino acids of the pocket contribute most to the binding and the type of energy of said binding. This work is an example of the contribution topological energy decomposition methods can make to fragment-based drug design.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Functional-Unit-Based Material Design: Ultralow Thermal Conductivity in Thermoelectrics with Linear Triatomic Resonant Bonds

We demonstrate the use of functional-unit-based material design for thermoelectrics. This is an efficient approach for identifying high-performance thermoelectric materials, based on the use of combinations of functional fragments relevant to desired properties. Here, we reveal that linear triatomic resonant bonds (LTRBs) found in some Zintl compounds provide strong anisotropy both structurally and electronically, along with strong anharmonic phonon scattering. An LTRB is thus introduced as a functional unit, and compounds are then screened as potential thermoelectric materials. We identify 17 semiconducting candidates from the MatHub-3d database that contain LTRBs. Detailed transport calculations demonstrate that the LTRB-containing compounds not only have considerably lower lattice thermal conductivities than other compounds with similar average atomic masses, but also exhibit remarkable band anisotropy near the valence band maximums due to the LTRB. K₅CuSb₂ is adopted as an example to elucidate the fundamental correlation between the LTRB and thermoelectric properties. The [Sb-Cu-Sb]^5- resonant structures demonstrate the delocalized Sb-Sb interaction within each LTRB, resulting in the softening of TA phonons and leading to large anharmonicity. The low lattice thermal conductivity (0.39 W/m·K at 300 K) combined with the band anisotropy results in a high thermoelectric figure of merit (ZT) for K₅CuSb₂ of 1.3 at 800 K. This work is a case study of the functional-unit-based material design for the development of novel thermoelectric materials.

19F chemical library and 19F-NMR for a weakly bound complex structure

Fragment-based drug discovery (FBDD), which involves small compounds <300 Da, has been recognized as one of the most powerful tools for drug discovery. In FBDD, the affinity of hit compounds tends to be low, and the analysis of protein-compound interactions becomes difficult. In an effort to overcome such difficulty, we developed a ¹⁹F-NMR screening method optimizing a ¹⁹F chemical library focusing on highly soluble monomeric molecules. Our method was successfully applied to four proteins, including protein kinases and a membrane protein. For FKBP12, hit compounds were carefully validated by protein thermal shift analysis, ¹H-¹⁵N HSQC NMR spectroscopy, and isothermal titration calorimetry to determine dissociation constants and model complex structures. It should be noted that the ¹H and ¹⁹F saturation transfer difference experiments were crucial to obtaining highly precise model structures. The combination of ¹⁹F-NMR analysis and the optimized ¹⁹F chemical library enables the modeling of the complex structure made up of a weak binder and its target protein.

Fragment-Based and Structural Investigation for Discovery of JNK3 Inhibitors

The c-Jun N-terminal kinases (JNKs) are members of the mitogen-activated protein kinase (MAPK) family and are related to cell proliferation, gene expression, and cell death. JNK isoform 3 (JNK3) is an important therapeutic target in varieties of pathological conditions including cancers and neuronal death. There is no approved drug targeting JNKs. To discover chemical inhibitors of JNK3, virtual fragment screening, the saturation transfer difference (STD) NMR, in vitro kinase assay, and X-ray crystallography were employed. A total of 27 fragments from the virtually selected 494 compounds were identified as initial hits via STD NMR and some compounds showed the inhibition of the activity of JNK3 in vitro. The structures of JNK3 with a fragment and a potent inhibitor were determined by X-ray crystallography. The fragment and inhibitor shared a common JNK3-binding feature. The result shows that fragment screening by NMR spectroscopy is a very efficient method to screen JNK3 binders and the structure of JNK3-inhibitor complex can be used to design and develop more potent inhibitors.

Tools shaping drug discovery and development

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

How does a small molecule bind at a cryptic binding site?

Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.

Small-molecule drugs typically function by binding to and modulating the biological activity of their protein targets. Drug-binding sites resemble pockets or grooves on the surface of the target protein, and are generally present even when the drug is not bound. In the case of “cryptic” binding sites, however, the pocket or groove only takes shape during the drug-binding process, prior to which the geometric features of a typical binding site are absent. Cryptic sites commonly occur at protein-protein interfaces, for example, so targeting such sites could facilitate the design of drugs capable of modulating specific protein-protein interactions—an approach with great therapeutic potential. In practice, targeting cryptic sites is typically difficult, in part because much less is known about how small molecules bind to cryptic sites than to conventional sites. In the work reported here, we used molecular dynamics simulations to study the process of a drug binding at a cryptic binding site, and showed that simulations are capable of predicting the location and geometry of a drug binding. The improved understanding of how small molecules bind at cryptic sites afforded by approaches like the one presented here could aid the rational design of small molecules that target such sites.

MicroED: conception, practice and future opportunities

This article documents a keynote seminar presented at the IUCr Congress in Prague, 2021. The cryo-EM method microcrystal electron diffraction is described and put in the context of macromolecular electron crystallography from its origins in 2D crystals of membrane proteins to today's application to 3D crystals a millionth the size of that needed for X-ray crystallography. Milestones in method development and applications are described with an outlook to the future.

Fragment-Based Drug Discovery for Trypanosoma brucei Glycosylphosphatidylinositol-Specific Phospholipase C through Biochemical and WaterLOGSY-NMR Methods

Glycosylphosphatidylinositol-specific phospholipase C (GPI-PLC) of Trypanosoma brucei, the causative protozoan parasite of African trypanosomiasis, is a membrane-bound enzyme essential for antigenic variation, because it catalyses the release of the membrane-bound form of variable surface glycoproteins. Here, we performed a fragment-based drug discovery of TbGPI-PLC inhibitors using a combination of enzymatic inhibition assay and water-ligand observed via gradient spectroscopy (WaterLOGSY) NMR experiment. The TbGPI-PLC was cloned and over-expressed using an Escherichia coli expression system followed by purification using three-phase partitioning and gel filtration. Subsequently, the inhibitory activity of 873 fragment compounds against the recombinant TbGPI-PLC led to the identification of 66 primary hits. These primary hits were subjected to the WaterLOGSY NMR experiment where 10 fragment hits were confirmed to directly bind to the TbGPI-PLC. These included benzothiazole, chlorobenzene, imidazole, indole, pyrazol and quinolinone derivatives. Molecular docking simulation indicated that six of them share a common binding site, which corresponds to the catalytic pocket. The present study identified chemically diverse fragment hits that could directly bind and inhibit the TbGPI-PLC activity which constructed a framework for fragment optimisation or linking towards the design of novel drugs for African trypanosomiasis.

Inverse design of 3d molecular structures with conditional generative neural networks

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.

Computer-aided identification of potential inhibitors against Necator americanus glutathione S-transferase 3

Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than -9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

Structure-based protein-ligand interaction fingerprints for binding affinity prediction

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.