Deep generative molecular design and its value in modern drug discovery

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

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.

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

Undoubtedly, deep learning (DL) has made striking progress in different artificial intelligence research fields in the bygone decade. DL is a subset of artificial intelligence, where models depict mathematical changes over a wide range of layers. DL is rapidly becoming the state-of-the-art execution in a variety of assignments and it has indicated huge potential in different fields, for example, speech recognition, computer vision and natural language processing. It has additionally been effectively applicable in drug discovery which is one of the areas that can acquire many advantages from the accomplishment of DL. Drug discovery and advancement are among the main translational science activities that add to human well-being and prosperity. This chapter highlights fundamentals of DL, challenges, opportunities, and applications in drug discovery.

The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a significant inflection point in AI development. Long viewed as the epitome of reasoning and logic, Generative AI incorporates programming rules that are normative. However, it also has a descriptive component based on its programmers' subjective preferences and any discrepancies in the underlying data. Generative AI generates both truth and falsehood, supports both ethical and unethical decisions, and is neither transparent nor accountable. These factors pose clear risks to optimal decision-making in complex health services such as health policy and health regulation. It is important to examine how Generative AI makes decisions both from a rational, normative perspective and from a descriptive point of view to ensure an ethical approach to Generative AI design, engineering, and use. The objective is to provide a rapid review that identifies and maps attributes reported in the literature that influence Generative AI decision-making in complex health services. This review provides a clear, reproducible methodology that is reported in accordance with a recognised framework and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards adapted for a rapid review. Inclusion and exclusion criteria were developed, and a database search was undertaken within four search systems: ProQuest, Scopus, Web of Science, and Google Scholar. The results include articles published in 2023 and early 2024. A total of 1,550articles were identified. After removing duplicates, 1,532articles remained. Of these, 1,511 articles were excluded based on the selection criteria and a total of 21 articles were selected for analysis. Learning, understanding, and bias were the most frequently mentioned Generative AI attributes. Generative AI brings the promise of advanced automation, but carries significant risk. Learning and pattern recognition are helpful, but the lack of a moral compass, empathy, consideration for privacy, and a propensity for bias and hallucination are detrimental to good decision-making. The results suggest that there is, perhaps, more work to be done before Generative AI can be applied to complex health services.

Artificial intelligence (AI) has grown widely into diverse areas of research over the last decade. Publications distributed over the years disclose that biochemistry and analytical chemistry are incorporating AI to the utmost level. Enormous complex data sets are produced by analytical instruments comprising a treasure of information which is very helpful to analytical chemists in characterization. Despite this, we lack capabilities in data analysis and algorithms, which restricts our aptitude to discover and employ this data entirely. Machine learning and AI are being instigated to deal with this current challenge by accelerating several other applications in analytical chemistry. A critical valuation of illustrative reports amalgamating AI with analytical chemistry, spectroscopies, and several sensors has been discussed widely. This comprehensive review evaluates the AI advancement in data interpretation, the assistance of AI in analyte identification and quantification in proteomics and metabolomics, AI databases for primary and secondary metabolites information, regulatory and ethical guidelines in analytical chemistry incorporating AI, machine learning applications in drug discovery, agriculture, cosmetic, and food and separation techniques. In summary, this current review puts forward a thorough examination concerning the advancements of AI in different domains of chemistry and strives to facilitate comprehension of its forthcoming trajectories.

Objective: Remote Sensing Image Captioning (RSIC) is a challenging research area that aims to automatically generate descriptive textual captions for Remote Sensing Images (RSIs) with recent advancements in deep learning, RSIC performance has significantly improved. Methods: This study presents a comparative analysis of two prominent approaches: Channel Attention Mechanism (CAM) and Generative AI (GenAI). The Channel Attention Mechanism emphasises on learning channel-wise with encoding-decoding, feature extraction and attention module. On the other hand, Generative AI, particularly models like GPT, leverages large-scale pretraining to diverse datasets for generates coherent and contextually relevant captions. It extracts vision-based feature and contains auto-tokenizer within the ViT Encoder-Decoder. Findings: This study evaluated on the RSICD dataset using metrics such as BLEU, METEOR, ROUGE-L, CIDEr, Precision, Recall, F1-Score, F2-Score, and Accuracy, GenAI outperforms CAM across all these metrics. Specifically, GenAI achieves a Precision of 0.90, Recall of 0.86, and Accuracy of 0.92. BLEU-4 scores for GenAI (0.7684) exceed CAM (0.4932), highlighting superior caption quality. However, both methods show limitations in accurately quantifying objects and specifying their coordinates within images. Novelty: The comparative analysis highlights the trade-off between traditional attention-based method and modern generative models. Keywords: Channel Attention Mechanism, Encoder-Decoder, Feature Extraction, Generative AI, Pre-Trained Models, RSICD Dataset

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

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Background: Recent progress in protein structure prediction by AlphaFold has opened new avenues to decipher biological functions from the perspective of structural biology based on the proteomics level. Methods: To meet these challenges, in this perspective, three scales of networks for protein structures, including structural protein-protein networks, protein structural networks, and elastic network models were introduced for high-throughput modeling of protein functional sites and protein dynamics. Conclusion: In the post-AlphaFold era, it is assumed that the integration of biomolecular networks may be leveraged in the future to develop a modeling framework that addresses protein structure-based functions with the application in drug discovery.

The evolution of generative AI, driven by advancements in large language models (LLMs) and deep learning, has revolutionized the field of code generation, offering transformative solutions for software development. This paper explores state-of- the-art frameworks like StepCoder and AI Programmer, which leverage reinforcement learning (RL) and genetic algorithms (GAs), respectively, to address challenges in generating complex code sequences and optimizing program functionality. StepCoder introduces a curriculum-based approach to break down lengthy code tasks and fine-grained optimization techniques to enhance code quality, while AI Programmer employs constrained programming languages and genome-based techniques to autonomously generate full software programs. Additionally, this study examines the integration of generative AI into competitive programming tasks and its ability to predict program functions by fusing neural network predictions with search algorithms. Generative AI tools streamline coding processes by automating repetitive tasks, modernizing legacy systems, and translating code across languages. These innovations not only enhance productivity but also democratize coding by making it accessible to developers of all skill levels. The paper further discusses the implications of generative AI in computer science education and professional programming, emphasizing the need for actionable strategies to harness its benefits while addressing ethical concerns and technical limitations. By analyzing experimental results and real-world applications, this research highlights how generative AI is reshaping software engineering, fostering collaboration between human creativity and AI-driven automation, and paving the way for more efficient and innovative coding practices

Rethinking hepatitis C viral kinetics: Insights into host-virus interactions in ‘difficult-to-treat’ groups and implications for novel treatment approaches

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

The rapid growth of genomic data presents new challenges and opportunities in the field of bioinformatics. Deep learning has emerged as a powerful tool for analyzing large and complex genomic datasets, offering unprecedented capabilities for identifying patterns, predicting gene functions, and understanding genetic variations. This article explores the latest advancements in deep learning techniques applied to genomic data analysis. It discusses key approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, and their applications in various aspects of genomics, including gene expression analysis, variant calling, and drug discovery. The article also highlights the integration of deep learning with other emerging technologies, such as CRISPR, and its potential to revolutionize personalized medicine and genetic research..

Review: [recent developments towards the construction of compound libraries and their application in drug and probe discovery; 86 refs.