Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design

Small-molecule natural product sophoricoside reduces peripheral neuropathic pain via directly blocking of NaV1.6 in dorsal root ganglion nociceptive neurons

Peripheral neuropathic pain poses a significant global health challenge. Current drugs for peripheral neuropathic pain often fall short in efficacy or come with severe side effects, emphasizing the critical need for the development of highly effective and well-tolerated alternatives. Sophoricoside (SOP) is a nature product-derived isoflavone that possesses various pharmacological effects on inflammatory and neuropathy diseases. Here, in this study, analgesic effect was investigated by intrathecally administration of SOP/vehicle to spared nerve injury (SNI) or paclitaxel-induced peripheral neuropathic pain (PINP) rodent models, and mechanical allodynia was measured in Von Frey tests. Ipsilateral L4-L6 dorsal root ganglia (DRG) were used for protein expression. In silico molecular docking analysis was applied for assessing compound-target binding affinity. Primary cultured DRG neurons were utilized to investigate SOP's effect on veratridine-triggered nociceptor activities and its selective inhibition of voltage-gated sodium channels subtype 1.6 (NaV1.6). The results showed SOP treatment alleviated mechanical allodynia in SNI and PINP rodent models (paw withdrawal threshold after 1 h of injection: SNI-vehicle: 1.385 ± 0.338 g; SNI-SOP: 9.963 ± 2.029 g, P < 0.001; PINP-vehicle: 5.040 ± 0.985 g; PINP-SOP: 8.287 ± 3.812 g, P = 0.004). SOP presented effects on both inhibiting veratridine-triggered nociceptor activities (oscillatory population: vehicle: 39.9 ± 7.3%; SOP: 30.7 ± 9.8%, P = 0.021) and selectively blocking NaV1.6 in DRG sensory neurons. Molecular docking analysis indicated direct binding between SOP and NaV1.6, leading to its endocytosis in DRG Sensory Neurons. In conclusion, SOP alleviated nociceptive allodynia induced by peripheral nerve injury via selectively blocking of NaV1.6 in DRG nociceptive neurons. we highlight its potential as an analgesic and elucidate its mechanism involving NaV1.6 endocytosis. This research opens avenues for exploring the analgesic effects of SOP and its potential impact on neuropathic pain therapy.

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compounds, with many successful drugs originating from them. Advances in information science expanded NP-related databases, enabling deeper exploration with AI. Integrating AI into NP drug discovery promises accelerated discoveries, leveraging AI's analytical prowess, including generative AI for data synthesis. This perspective illuminates AI's current landscape in NP drug discovery, addressing strengths, limitations, and future trajectories to advance this vital research domain.

We are MedChem: The Frontiers in Medicinal Chemistry 2024

The Frontiers in Medicinal Chemistry (FiMC) is the largest international Medicinal Chemistry conference in Germany and took place from March 17th to 20th 2024 in Munich. Co-organized by the Division of Medicinal Chemistry of the German Chemical Society (Gesellschaft Deutscher Chemiker; GDCh) and the Division of Pharmaceutical and Medicinal Chemistry of the German Pharmaceutical Society (Deutsche Pharmazeutische Gesellschaft; DPhG), and supported by a local organizing committee from the Ludwigs-Maximilians-University Munich headed by Daniel Merk, the meeting brought together approximately 225 participants from 20 countries. The outstanding program of the four-day conference included 40 lectures by leading scientists from industry and academia as well as early career investigators. Moreover, 100 posters were presented in two highly interactive poster sessions.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Combining de novo molecular design with semiempirical protein-ligand binding free energy calculation

Semi-empirical quantum chemistry methods estimate the binding free energies of protein-ligand complexes. We present an integrated approach combining the GFN2-xTB method with de novo design for the generation and evaluation of potential inhibitors of acetylcholinesterase (AChE). We employed chemical language model-based molecule generation to explore the synthetically accessible chemical space around the natural product Huperzine A, a potent AChE inhibitor. Four distinct molecular libraries were created using structure- and ligand-based molecular de novo design with SMILES and SELFIES representations, respectively. These libraries were computationally evaluated for synthesizability, novelty, and predicted biological activity. The candidate molecules were subjected to molecular docking to identify hypothetical binding poses, which were further refined using Gibbs free energy calculations. The structurally novel top-ranked molecule was chemically synthesized and biologically tested, demonstrating moderate micromolar activity against AChE. Our findings highlight the potential and certain limitations of integrating deep learning-based molecular generation with semi-empirical quantum chemistry-based activity prediction for structure-based drug design.

Tailored quinoline hybrids as promising COX-2/15-LOX dual inhibitors endowed with diverse safety profile: Design, synthesis, SAR, and histopathological study

Complications of the worldwide use of non-steroidal anti-inflammatory drugs (NSAIDs) sparked scientists to design novel harmless alternatives as an urgent need. So, a unique hybridization tactic of quinoline/pyrazole/thioamide (4a-c) has been rationalized and synthesized as potential COX-2/15-LOX dual inhibitors, utilizing relevant reported studies on these pharmacophores. Moreover, we extended these preceding hybrids into more varied functionality, bearing crucial thiazole scaffolds(5a-l). All the synthesized hybrids were evaluatedin vitroas COX-2/15-LOX dual inhibitors. Initially, series4a-cexhibited significant potency towards 15-LOX inhibition (IC50 = 5.454-4.509 μM) compared to meclofenamate sodium (IC50 = 3.837 μM). Moreover, they revealed reasonable inhibitory activities against the COX-2 enzyme in comparison to celecoxib.Otherwise, conjugates 5a-ldisclosed marked inhibitory activity against 15-LOX and strong inhibitory to COX-2. In particular, hybrids5d(IC50 = 0.239 μM, SI = 8.95), 5h(IC50 = 0.234 μM, SI = 20.35) and 5l (IC50 = 0.201 μM, SI = 14.42) revealed more potency and selectivity outperforming celecoxib (IC50 = 0.512 μM, SI = 4.28). In addition, the most potentcompounds, 4a, 5d, 5h, and 5l have been elected for further in vivoevaluation and displayed potent inhibition of edema in the carrageenan-induced rat paw edema test that surpassed indomethacin. Further, compounds5d, 5h, and 5l decreased serum inflammatory markers including oxidative biomarkersiNO, and pro-inflammatory mediators cytokines like TNF-α, IL-6, and PGE. Ulcerogenic liability for tested compounds demonstrated obvious gastric mucosal safety. Furthermore, a histopathological study for compound 5l suggested a confirmatory comprehensive safety profile for stomach, kidney, and heart tissues. Docking and drug-likeness studies offered a good convention with the obtained biological investigation.

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

Effectiveness of molecular fingerprints for exploring the chemical space of natural products

Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints .

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

A further pocket or conformational plasticity by mapping COX-1 catalytic site through modified-mofezolac structure-inhibitory activity relationships and their antiplatelet behavior

Cyclooxygenase enzymes have distinct roles in cardiovascular, neurological, and neurodegenerative disease. They are differently expressed in different type of cancers. Specific and selective COXs inhibitors are needed to be used alone or in combo-therapies. Fully understand the differences at the catalytic site of the two cyclooxygenase (COX) isoforms is still opened to investigation. Thus, two series of novel compounds were designed and synthesized in fair to good yields using the highly selective COX-1 inhibitor mofezolac as the lead compound to explore a COX-1 zone formed by the polar residues Q192, S353, H90 and Y355, as well as hydrophobic amino acids I523, F518 and L352. According to the structure of the COX-1:mofezolac complex, hydrophobic amino acids appear to have free volume eventually accessible to the more sterically hindering groups than the methoxy linked to the phenyl groups of mofezolac, in particular the methoxyphenyl at C4-mofezolac isoxazole. Mofezolac bears two methoxyphenyl groups linked to C3 and C4 of the isoxazole core ring. Thus, in the novel compounds, one or both methoxy groups were replaced by the higher homologous ethoxy, normal and isopropyl, normal and tertiary butyl, and phenyl and benzyl. Furthermore, a major difference between the two sets of compounds is the presence of either a methyl or acetic moiety at the C5 of the isoxazole. Among the C5-methyl series, 12 (direct precursor of mofezolac) (COX-1 IC50 = 0.076 μM and COX-2 IC50 = 0.35 μM) and 15a (ethoxy replacing the two methoxy groups in 12; COX-1 IC50 = 0.23 μM and COX-2 IC50 > 50 μM) were still active and with a Selectivity Index (SI = COX-2 IC50/COX-1 IC50) = 5 and 217, respectively. The other symmetrically substituted alkoxyphenyl moietis were inactive at 50 μM final concentration. Among the asymmetrically substituted, only the 16a (methoxyphenyl on C3-isoxazole and ethoxyphenyl on C4-isoxazole) and 16b (methoxyphenyl on C3-isoxazole and n-propoxyphenyl on C4-isoxazole) were active with SI = 1087 and 38, respectively. Among the set of compounds with the acetic moiety, structurally more similar to mofezolac (SI = 6329), SI ranged between 1.4 and 943. It is noteworthy that 17b (n-propoxyphenyl on both C3- and C4-isoxazole) were found to be a COX-2 slightly selective inhibitor with SI = 0.072 (COX-1 IC50 > 50 μM and COX-2 IC50 = 3.6 μM). Platelet aggregation induced by arachidonic acid (AA) can be in vitro suppressed by the synthesized compounds, without affecting of the secondary hemostasia, confirming the biological effect provided by the selective inhibition of COX-1. A positive profile of hemocompatibility in relation to erythrocyte and platelet toxicity was observed. Additionally, these compounds exhibited a positive profile of hemocompatibility and reduced cytotoxicity. Quantitative structure activity relationship (QSAR) models and molecular modelling (Ligand and Structure based virtual screening procedures) provide key information on the physicochemical and pharmacokinetic properties of the COX-1 inhibitors as well as new insights into the mechanisms of inhibition that will be used to guide the development of more effective and selective compounds. X-ray analysis was used to confirm the chemical structure of 14 (MSA17).

Prophylaxis of posterior capsule opacification through autophagy activation with indomethacin-eluting intraocular lens

Posterior capsule opacification (PCO) is the most common long-term postoperative complication of cataract surgery, leading to secondary vision loss. Optimized intraocular lens (IOL) structure and appropriate pharmacological intervention, which provides physical barriers and biological inhibition, respectively, can block the migration, proliferation, and epithelial-mesenchymal transition (EMT) of lens epithelial cells (LECs) for PCO prophylaxis. Herein, a novel indomethacin-eluting IOL (INDOM-IOL) with an optimized sharper edge and a sustained drug release behavior was developed for PCO prevention. Indomethacin (INDOM), an ophthalmic non-steroidal anti-inflammatory drug (NSAID) used for postoperative ocular inflammation, was demonstrated to not only be able to suppress cell migration and down-regulate the expression of cyclooxygenase-2 (COX-2) and EMT markers, including alpha-smooth muscle actin (α-SMA) and cyclin D1, but also promote the autophagy activation in LECs. Additionally, autophagy was also verified to be a potential therapeutic target for the down-regulation of EMT in LECs. The novel IOL, serving as a drug delivery platform, could carry an adjustable dose of hydrophobic indomethacin with sustained drug release ability for more than 28 days. In the rabbit PCO model, the indomethacin-eluting IOL showed excellent anti-inflammatory and anti-PCO effects. In summary, indomethacin is an effective pharmacological intervention in PCO prophylaxis, and the novel IOL we developed prevented PCO in vivo under its sustained indomethacin release property, which provided a promising approach for PCO prophylaxis in clinical application.

SC-560 and mofezolac isosteres as new potent COX-1 selective inhibitors with antiplatelet effect

Selective cyclooxygenase (COX)-1 inhibitors can be employed as potential cardioprotective drugs. Moreover, COX-1 plays a key role in inflammatory processes and its activity is associated with some types of cancer. In this work, we designed and synthesized a set of compounds that structurally mimic the selective COX-1 inhibitors, SC-560 and mofezolac, the central cores of which were replaced either with triazole or benzene rings. The advantage of this approach is a relatively simple synthesis in comparison with the syntheses of parent compounds. The newly synthesized compounds exhibited remarkable activity and selectivity toward COX-1 in the enzymatic in vitro assay. The most potent compound, 10a (IC50  = 3 nM for COX-1 and 850 nM for COX-2), was as active as SC-560 (IC50  = 2.4 nM for COX-1 and 470 nM for COX-2) toward COX-1 and it was even more selective. The in vitro COX-1 enzymatic activity was further confirmed in the cell-based whole-blood antiplatelet assay, where three out of four selected compounds (10a,c,d, and 3b) exerted outstanding IC50 values in the nanomolar range (9-252 nM). Moreover, docking simulations were performed to reveal key interactions within the COX-1 binding pocket. Furthermore, the toxicity of the selected compounds was tested using the normal human kidney HK-2 cell line.

2,3-Dihydrosorbicillin and chrysopanol stimulate insulin secretion in INS-1 cells

Pancreatic β-cell function and insulin secretion are important in antidiabetic drug development. In an effort to discover small molecules to regulate insulin secretion, an endophytic fungus, Penicillium sp. SSP-1CLG, was selected for chemical investigation. Large scale cultures of the strain followed by extraction and chromatographic analysis led to the isolation of 10 anthraquinone and alkaloid-type compounds. The isolated compounds were identified by comprehensive analysis of NMR, MS, and ECD data. The effect of compounds 1-10 on insulin secretion in INS-1 cells was investigated. 2,3-Dihydrosorbicillin (1), chrysophanol (2), and glandicolin B (10) at non-cytotoxic concentrations resulted in an increase of glucose-stimulated insulin secretion (GSIS) in rat INS-1 pancreatic β-cells. Furthermore, we investigated the signaling pathway involved in 2,3-dihydrosorbicillin (1) and chrysophanol (2) action in the activation of peroxisome proliferator-activated receptor γ (PPARγ), pancreatic and duodenal homeobox-1 (PDX-1), insulin receptor substrate-2 (IRS-2), phosphatidylinositol 3-kinase (PI3K), and Akt. Treatment of INS-1 cells with 2,3-dihydrosorbicillin (1) and chrysophanol (2) increased the expression of these proteins. Our findings indicate that 2,3-dihydrosorbicillin and chrysophanol may play roles in the regulation of insulin secretion in pancreatic β-cells, at least in part, by targeting PPARγ and PDX-1 via the IRS-2/PI3K/Akt signaling pathway.

Allosteric Activation of 15-Lipoxygenase-1 by Boswellic Acid Induces the Lipid Mediator Class Switch to Promote Resolution of Inflammation

Specialized pro-resolving mediators (SPM), primarily produced in innate immune cells, exert crucial bioactions for resolving inflammation. Among various lipoxygenases (LOX), 15-LOX-1 is key for SPM biosynthesis, but cellular activation principles of 15-LOX-1 are unexplored. It was shown that 3-O-acetyl-11-keto-β-boswellic acid (AKBA) shifts 5-LOX regiospecificity from 5- to 12-lipoxygenation products. Here, it is demonstrated that AKBA additionally activates cellular 15-LOX-1 via an allosteric site accomplishing robust SPM formation in innate immune cells, particularly in M2 macrophages. Compared to ionophore, AKBA-induced LOX activation is Ca2+ - and phosphorylation-independent, with modest induction of 5-LOX products. AKBA docks into a groove between the catalytic and regulatory domains of 15-LOX-1 interacting with R98; replacement of R98 by alanine abolishes AKBA-induced 15-LOX product formation in HEK293 cells. In zymosan-induced murine peritonitis, AKBA strikingly elevates SPM levels and promotes inflammation resolution. Together, targeted allosteric modulation of LOX activities governs SPM formation and offers new concepts for inflammation resolution pharmacotherapy.

Reversal of Peripheral Neuropathic Pain by the Small-Molecule Natural Product Narirutin via Block of Nav1.7 Voltage-Gated Sodium Channel

Neuropathic pain is a refractory chronic disease affecting millions of people worldwide. Given that present painkillers have poor efficacy or severe side effects, developing novel analgesics is badly needed. The multiplex structure of active ingredients isolated from natural products provides a new source for phytochemical compound synthesis. Here, we identified a natural product, Narirutin, a flavonoid compound isolated from the Citrus unshiu, showing antinociceptive effects in rodent models of neuropathic pain. Using calcium imaging, whole-cell electrophysiology, western blotting, and immunofluorescence, we uncovered a molecular target for Narirutin's antinociceptive actions. We found that Narirutin (i) inhibits Veratridine-triggered nociceptor activities in L4-L6 rat dorsal root ganglion (DRG) neurons, (ii) blocks voltage-gated sodium (Na_V) channels subtype 1.7 in both small-diameter DRG nociceptive neurons and human embryonic kidney (HEK) 293 cell line, (iii) does not affect tetrodotoxin-resistant (TTX-R) Na_V channels, and (iv) blunts the upregulation of Na_v1.7 in calcitonin gene-related peptide (CGRP)-labeled DRG sensory neurons after spared nerve injury (SNI) surgery. Identifying Na_v1.7 as a molecular target of Narirutin may further clarify the analgesic mechanism of natural flavonoid compounds and provide an optimal idea to produce novel selective and efficient analgesic drugs.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.