Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Introduction Deep generative models are reshaping de novo drug design by enabling creation of novel, property-optimized molecules beyond traditional chemical libraries. Advances in deep learning, molecular representation learning, and structure-aware modeling now enable algorithms to propose molecules that satisfy complex pharmacological constraints, accelerating hit identification. Areas covered This review outlines recent advances in generative molecular design, including neural network-based frameworks, reinforcement learning systems, diffusion models, and language model-based transformers. The authors outline how each class generates and optimizes molecular structures and review generative AI’s practical applications in drug discovery, illustrating translational progress. Current bottlenecks are critically analyzed alongside emerging solutions. This review is based on a systematic literature search conducted in Google Scholar and PubMed, covering studies published up to December 2025. Expert opinion Generative AI’s greatest promise lies not in generating more molecules, but in generating better hypotheses, structures that are synthetically accessible, biologically plausible, optimized across potency, selectivity, and pharmacokinetics. The next phase will be led by multimodal foundation models capable of reasoning jointly about chemistry, protein structure, and cellular response, supported by automated synthesis and high-throughput experimentation. As these components are integrated, generative molecular design will guide lead optimization and reshape how new therapies are discovered and developed.

Exploring new horizons: Empowering computer-assisted drug design with few-shot learning

Methods for compound selection focused on hits and application in drug discovery

The computer modelling of structural complexes generated from two or more interacting molecules are referred to as molecular docking. It is an indispensable tool in computer-aided drug design and structural molecular biology. Using this technology, large libraries of compounds may be digitally screened, and the results can be graded along with structural assumptions about how the ligands impact the target's reduction. Recent advances in the synthesis of anti-infectious medicines prompted by structural insights have enabled the application of computer-assisted drug design in the quest for innovative mechanism- or structure-based drugs. Molecular docking is an important phase in the drug development process because it determines the best positions for molecules to occupy when they are coupled together and predicts how effectively two molecules will bind once they have been docked. The input structure's design is also critical, and the results are assessed using sampling methods and scoring systems. The recently developed docking software Local Move Monte Carlo [LMMC] provides a strong choice for customizable receptor docking strategies. Docking is a technique for determining how ligands and proteins interact. It is structurally sound and compatible with computer-assisted medication design. Successful docking discovers high-dimensional spaces and ranks function utilisation, resulting in a candidate docking rating that is acceptable. It may also be used to screen vast libraries of molecules and offer structural hypotheses for the process.

Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR)

Ethnopharmacology is the study of traditional medicinal knowledge and its application in modern drug discovery. It combines ethnobotanical insights with scientific research to identify bio-active compounds with therapeutic potential. Sustainable agriculture refers to farming practices that maintain ecological balance, support biodiversity, and ensure the long-term availability of resources. Integrating these fields can enhance drug discovery while preserving medicinal plants and promoting environmental sustainability. This review examines the collaboration between ethnopharmacology and sustainable agriculture in advancing drug discovery, conservation, and global food security. This review examines the role of ethnopharmacology in drug discovery, analyzing traditional medicinal practices, bioactivity-guided fractionation, and metabolomic profiling. It also investigates sustainable agriculture techniques, including organic farming, controlled cultivation, and conservation strategies for medicinal plants. Data were collected from peer-reviewed literature using sources such as Google Scholar, PubMed, Scopus, and journal databases like ScienceDirect. Ethnopharmacology has con-tributed to the discovery of key drugs with anticancer, anti-inflammatory, and antimicrobial proper-ties. Sustainable agriculture ensures a steady supply of medicinal plants while optimizing their bio-active compound production through improved cultivation techniques. The combination of these ap-proaches strengthens drug discovery efforts and supports ecological conservation. Integrating eth-nopharmacology with sustainable agriculture is a promising strategy for developing new drugs while protecting natural resources. Future research should focus on innovative cultivation techniques, com-munity-led conservation efforts, and advanced analytical methods to enhance the discovery of new drugs. The adoption of agroecological practices, technological advancements, and policy support will be crucial in ensuring sustainable and equitable benefits for healthcare and agriculture. Bridging tra-ditional knowledge with scientific research will foster new therapeutic discoveries while promoting environmental sustainability.

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

Software based approaches for drug designing and development: A systematic review on commonly used software and its applications

Molecular modeling soon attracted the attention of Medicinal Chemistry researchers, given the importance of molecular structure for understanding the mode of action and for designing bioactive compounds. Computer-assisted drug design (CADD) has become widespread and today big pharmaceutical companies routinely uses it as a support tool in the search for new drugs. Here, it will be addressed the most relevant topics about CADD from the last 30 years, when research groups in Medicinal Chemistry began to explore molecular modeling in Brazil. This history can be described through phases in which some methods emerged and became predominant, in a continuous evolution, passing, for example, from the basic empirical field and quantum mechanics molecular modeling procedures, through quantitative structure-activity relationship (QSAR) methods, molecular docking and, more recently, virtual screening. Since the mid-2000s, machine learning methods have been increasingly applied to the solution of problems in the context of Medicinal Chemistry, such as the determination of protein 3D structure and the characterization of relationships between chemical structures and their biological activities. Far from being complete, this history continues its evolution, bringing significant contributions to the drug design, either by reducing the time and cost of research, or by enabling and accelerating the finding for new bioactive compounds.

Introduction Drug discovery is a complex and multifaceted process driven by scientific innovation and advanced technologies. Next-Generation Sequencing (NGS) platforms, encompassing both short-read and long-read technologies, have revolutionized the field by enabling the high-throughput and cost-effective analysis of DNA and RNA molecules. Continuous advancements in NGS-based technologies have enabled their seamless integration across preclinical and clinical workflows in drug discovery, encompassing early-stage drug target identification, candidate selection, genetically stratified clinical trials, and pharmacogenetic studies. Area covered This review provides an overview of the current and potential applications of NGS-based technologies in drug discovery and development process, including their roles in novel drug target identification, high-throughput screening, clinical trials, and clinical medication studies. The review is based on literature retrieval from the PubMed and Web of Science databases between 2018 and 2024. Expert opinion As technologies advance rapidly, NGS enhances accuracy and generates vast datasets. These datasets are extensively integrated with other heterogeneous data in systems biology and are mined using machine learning to extract significant insights, thereby driving progress in drug discovery.

Accurately predicting the binding affinities of large sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify potential drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein’s binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited scoring and ranking accuracies have been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we examine a range of SFs employing different machine-learning (ML) approaches in conjunction with a variety of physicochemical and geometrical features characterizing protein-ligand complexes. We compare the scoring and ranking accuracies of these ML SFs as well as those of conventional SFs in the context of the diverse test sets of the 2007 and 2010 PDBbind benchmarks. We also investigate the influence of the size of the training dataset and the number of features used on scoring and ranking accuracies. We find that the best performing ML SF has a scoring power of 0.807 in terms of Pearson correlation coefficient between predicted and measured binding affinities compared to 0.644 achieved by a state-of-the-art conventional SF. Despite this substantial improvement (25%) in binding affinity prediction, the ranking power improvement is only 6% from a success rate of 58.5% achieved by the best conventional SF to 62.2% obtained by the best ML approach when ligands were ranked for 65 unique proteins.

We discuss recent advancements in structural biology methods for investigating sites of protein-protein interactions. We will inform readers outside the field of structural biology about techniques beyond crystallography, and how these different technologies can be utilized for drug development. Advancements in cryo-electron microscopy (cryoEM) and micro-electron diffraction (microED) may change how we view atomic resolution structural biology, such that well-ordered macrocrystals of protein complexes are not required for interface identification. However, some drug discovery applications, such as lead peptide compound generation, may not require atomic resolution; mass spectrometry techniques can provide an expedited path to generation of lead compounds. New crosslinking compounds, more user-friendly data analysis, and novel protocols such as protein painting can advance drug discovery programs, even in the absence of atomic resolution structural data. Finally, artificial intelligence and machine learning methods, while never truly replacing experimental methods, may provide rational ways to stratify potential druggable regions identified with mass spectrometry into higher and lower priority candidates. Electron diffraction of nanocrystals combines the benefits of both x-ray diffraction and cryoEM, and may prove to be the next generation of atomic resolution protein-protein interface identification. However, in situations such as peptide drug discovery, mass spectrometry techniques supported by advancements in computational methods will likely prove sufficient to support drug discovery efforts. In addition, these methods can be significantly faster than any crystallographic or cryoEM methods for identification of interacting regions.

The acid-base dissociation constant (pKa) characterizes a molecule's tendency to donate or accept protons, thereby fundamentally influencing its physicochemical properties and behavior. Consequently, pKa is pivotal to diverse applications in drug discovery, including virtual screening and drug design. However, accurate pKa prediction remains challenging, due to intricate atomic interactions among ionizable groups and the scarcity of experimental data. To address these, we propose TripKa, a quantum-informed aqueous pKa predictor that employs a triplet interaction network to model complex thermodynamic coupling in multisite protonation equilibria. TripKa is fine-tuned using quantum-level descriptors as training targets, enabling the extraction of physical insights into molecular acid-base dissociation properties from limited high-quality pKa data. Our proposed TripKa outperforms state-of-the-art methods on multiple pKa benchmarks, including the SAMPL6-8 and Novartis data sets, achieving reductions of over 10% in both mean absolute error and root-mean-square error. Beyond pKa prediction, TripKa further serves as a foundational model for diverse molecular properties prediction and interaction modeling tasks, benefiting from its chemically informed pKa-pretraining.

Virtual screening is a standard tool in Computer-Assisted Drug Design (CADD). Early in a project, it is typical to use ligand-based similarity search methods to find suitable hit molecules. However, the number of compounds which can be screened and the time required are usually limited by computational resources. We describe here a high-throughput virtual screening project using 3D similarity (FastROCS) and automated evaluation workflows on Orion, a cloud computing platform. Cloud resources make this approach fully scalable and flexible, allowing the generation and search of billions of virtual molecules, and give access to an explicit 3D virtual chemistry space not available before. We discuss the impact of the size of the search space with respect to finding novel chemical hits and the size of the required hit list, as well as computational and economical aspects of resource scaling.

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient leaddiscovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than thetraditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledgeof the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applicationsof Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initialstages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoringhits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, inducedfit and consensus docking are also discussed. The review highlights advances in the field within the framework ofseveral success studies that have led to nM inhibition directly from VS and provides recent trends in library design as wellas discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enablethe discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteinsare also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increaseinhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed toinhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for theRXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on bindingsite shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein targetthrough the SBVS process.