Psychoactive Plant Database: A Phytochemical Resource for Neurological Drug Discovery

Harjot KaurMonika GuptaZabeer Ahmed^*Amit Nargotra^*

• Indian Institute of Integrative Medicine (CSIR), Jammu, India

Psychoactive plants are rich sources of bioactive compounds that modulate the central nervous system (CNS) activity and have shown therapeutic potential for neurological and psychiatric disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), epilepsy, and depression. These disorders pose significant global health challenges, necessitating the development of innovative therapeutic strategies [Puri et al., 2022;Feigin et al., 2020]. Various plant families, such as Solanaceae (Atropa belladonna L., Datura stramonium L.), Papaveraceae (Papaver somniferum L.), and Fabaceae (Mimosa pudica L.), produce neuroactive phytochemicals with well-documented CNS effects [Moise et al., 2024;Gutiérrez-Del-Río et al., 2021 ]. Several classes of phytochemicals have been identified as potential therapeutic agents for neurological disorders. Alkaloids such as scopolamine and hyoscyamine, exert anticholinergic properties [Kohnen-Johannsen et al., 2019 ], while morphine, an opiate alkaloid, remains a potent analgesic [Khademi et al., 2016]. Phenolic compounds, including quercetin and curcumin, demonstrate neuroprotective properties by attenuating oxidative stress and neuroinflammation [Rojas-García et al., 2023]. Additionally, terpenoids, such as cannabidiol (CBD) from Cannabis sativa L., exhibit anxiolytic, anti-inflammatory, and neuroprotective effects, modulating key neurotransmitter pathways such as dopaminergic and serotonergic signaling [Stasiłowicz-Krzemień et al., 2024, Rehman et al., 2019].The significance of plant-derived compounds in drug discovery is well-established, with approximately 25% of clinically approved drugs originating from natural products. Notably, natural compounds contribute to 60% of anticancer agents and 70% of anti-infective drugs, highlighting their continued relevance in modern pharmacology [Nasim et al., 2022;Asma et al., 2022;Newman et al., 2020]. Moreover, over 30% of pharmaceuticals currently in clinical trials are derived from natural sources, emphasizing the role of plantbased bioactive compounds in advancing novel therapeutic interventions.Despite the therapeutic potential of psychoactive plant-derived compounds for neurological and neurodegenerative disorders, existing knowledge on these phytochemicals remains fragmented and dispersed across multiple sources. This lack of a centralized, disease-specific database poses significant challenges in drug discovery and mechanistic research [Domingo-Fernández D. et al., 2024;Kim Y. C. 2010;Andrade S. et al., 2023]. There are a few phytochemical databses such as the Dictionary of Natural Products (DNP) [http://dnp.chemnetbase.com], KNApSAcK [Afendi et al., 2012], Dr. Duke's Phytochemical and Ethnobotanical Databases [http://phytochem.nal.usda.gov], and IMPPAT [Vivek-Ananth et al., 2023], but most of these provide data on plant-derived compounds in general, and lack disease-specific insights. Other databases susch as CANNUSE [Balant et al., 2021] is focused only on cannabis-related compounds, while EROWID [Erowid.org] provides generalized data of only the psychoactive substance [Gonçalves et al., 2021;Ryan, 2020;Spinks et al., 2011;Khadka et al., 2020].To address these limitations, the Psychoactive Plant Database (PPD) have been developed, which is a specialized platform focused on all the known phytochemicals isolated from psychoactive plants with a specific emphasis on their role in neurological disorders. Besides these features, the database also provides the correlation of all the phytochemicals with the existing drugs, which can be very useful information for drug repurposing.PPD serves as the first disease-centric database that integrates ethnobotanical knowledge with computational pharmacology, providing a scientifically rigorous and disease-relevant platform for researchers.By consolidating fragmented information into a single, structured resource, PPD facilitates interdisciplinary collaboration which can accelerate the discovery of novel neuroprotective agents for neurological and neurodegenerative disorders.The Among the 124 selected psychoactive plants, 31 species-including Piper longum L. and Withania somnifera (L.) Dunal-have been historically used in formulations targeting nervous system disorders.Notably, the Solanaceae family stands out with 14 psychoactive plants, enriched with tropane alkaloids such as scopolamine and hyoscyamine, which are well known for their neurological applications [Jaremicz Z. et al., 2014].To enhance the therapeutic relevance of the database, in silico ADMET profiling (Absorption, Distribution, Metabolism, Excretion, and Toxicity) was carried out, with a particular focus on CNS activity. It was found that 29% of the phytochemicals exhibited predicted CNS-active properties, with favorable pharmacokinetics, including molecular weights below 500 g/mol, making them promising candidates for further neurotherapeutic exploration. Furthermore, molecular docking studies were performed against NLRP3, a key target in neuroinflammation and neurodegenerative diseases, to find out the binding affinity of all the phytochemicals against this important neuroinflammation target. Several compounds demonstrated strong binding affinity (low docking scores), highlighting their potential as neuroprotective agents. Docking analysis plays a crucial role in predicting molecular interactions, allowing for the identification of lead compounds with high target specificity and binding strength. These computational insights help prioritize phytochemicals for in vitro and in vivo validation, accelerating their potential translation into therapeutic agents for neurological disorders.. The database also integrates structural and pharmacological information for each collected phytochemical, including: Molecular weight, Biological activity, Biological source, Chemical classification, Literature references etc. To further bridge computational insights with drug discovery, a similarity search was performed between all PPD phytochemicals and FDA-approved drugs, facilitating the identification of structurally related compounds that may advance into preclinical and clinical drug development stages (drug repurposing).To ensure efficient data retrieval, PPD offers a comprehensive search interface, allowing users to query the database by: Plant name, Molecular weight range, CAS ID, Chemical name, and Reported bioactivity. Users can perform exclusive or combined searches, so as to prune down to selective number of phytoconstituents with respect to theie associated properties. By integrating traditional ethnobotanical knowledge with computational insights, PPD serves as a valuable open-access resource for identifying potential candidates for neurodegenerative diseases, bridging traditional medicine with modern science, and accelerating natural product-based drug discovery for neurological disorders.The data of the psychoactive plants and their phytochemicals was obtained from four different authenticated [Zhang W. et al., 2024] (Fig. S1,S2).Traditional medicinal knowledge of all these plants was integrated from the Traditional Knowledge Digital Library (TKDL), which revealed that 31 medicinal plants are involved in treatments for rheumatism, nervous system disorders, urinary diseases, pain, tuberculosis, and cognitive impairments (Fig. S3) [Sharma Seemantani et al., 2017]. Examples include Piper longum L., Terminalia bellirica (Gaertn.) Roxb., Cannabis sativa L., Datura stramonium L., Acorus calamus L.,and Withania somnifera (L.) Dunal, which have been historically used in Ayurvedic formulations [Choudhary N. et al., 2018;Tiwana G. et al., 2024;Odieka A.E. et al., 2022;Soni P. et al., 2012;Sharma V. et al., 2020;Mukherjee P.K. et al., 2021]. Twenty-five percent of the plants in the database are reportedly used in various traditional formulations (Fig. S4), highlighting the ethnopharmacological significance of these species in guiding drug discovery [Pirintsos S. et al., 2022].To ensure accuracy, phytochemical data were cross-referenced with PubChem, DNP, KNApSAcK, Dr.Duke's database, and IMPPAT. All structures were verified using PubChem [Kim S. et al., 2023]. Unique identifiers (PPD_IDs) were assigned to each phytochemical. High-quality 2D molecular structures were generated using Schrödinger Maestro [Mumtaz A. et al., 2017]. An open-source database based on MySQL, PHP, and Apache was developed with filtering capabilities for plant names, CAS IDs, molecular weights, and bioactivity data [Sharma A. et al., 2014]. Analysis of the database revealed that close to 3,000 phytochemicals have molecular weight <500 g/mol (Fig. S5), thereby aligning with drug-likeness criteria.1,887 phytochemicals were already reported to have activity against multiple nervous disorders (Fig. S6). ADMET analysis of all the phytochemicals was conducted using QikProp (Schrödinger 2020-3), which revealed that more than 2000 phytochemicals (~30%) falls under CNS active criteria.These phytochemicals could be promising candidates to be taken forward as they already indicate neuroactive potential [Guan L et al., 2018]. Compounds like galantamine and berberine exhibited favorable pharmacokinetics, supporting their therapeutic relevance in AD and PD [Rezaul Islam M. et al., 2024].Molecular docking studies were conducted on NLRP3 (PDB ID: 7ALV), a neuroinflammatory target. Protein structures were refined using the Protein Preparation Wizard to optimize hydrogen bonding, loop regions, and energy minimization [Jin T. et al., 2023]. Docking validation involved re-docking the cocrystallized inhibitor to ensure accurate binding-site prediction. Ligands were prepared in LigPrep (OPLS_2005 force field), and XP docking in Glide was performed for precision [Friesner R. A. et al., 2006].Among the 7,000 phytochemicals, 250 exhibited strong binding energy, with 125 scoring below -11 kcal/mol. Pertinent to mention that lower the binding energy, more better is the target inhibition.Notably, Tellimargradin I (Juglans regia L.) demonstrated interactions with Ala227 and Arg578, similar to MCC950, an NLRP3 inhibitor (Fig. S8) [Blevins H. M. et al., 2022]. This information could be very useful for medicinal chemists for selecting the appropriate functional groups while designing potential inhibitors based on the listed phytochemicals.Using ClassyFire, the phytochemicals were classified into 175 chemical categories (Table S2). Major classes include prenol lipids (25%), alkaloids (9%), flavonoids (7%), terpenoids (8%), and organooxygen compounds(7%), which are known for their neuroprotective properties [Djoumbou Feunang Y. et al., 2016;Xu B. et al., 2022].Tanimoto similarity analysis (threshold 0.7) with the FDA-approved DrugBank library identified 371 phytochemicals with structural resemblance to approved drugs. This information could be very useful for drug repurposing (Table S3). Morphine (100% similarity with DB00295) and caffeine exemplify natural compounds with cognitive-enhancing potential [Bajusz D. et al., 2015;Cummings J. et al., 2024].The PPD is a user-friendly, open-source platform designed for modern research. It features: Search functionalities for plant names, molecular weights, CAS IDs, bioactivity, and chemical identifiers(InChI, SMILES). Substructure and similarity search tools for identifying related phytochemicals. Plant image galleries and interactive visualizations to enhance engagement (Fig. 1). Regular updates to integrate new research on psychoactive plants.PPD serves as a resource for identifying potential phytochemicals, integrating traditional knowledge, and facilitating drug discovery. With its structured data retrieval system, it would accelerate research in neurodegenerative diseases, ethnomedicine, and personalized medicine. Although some physicochemical properties are marked as "N/A" due to missing data, the database remains an invaluable tool for scientific discovery, bridging traditional knowledge with modern research. A search for the phytochemical "1-acetyl-7-hydroxy-beta-carboline; me ether" (CAS ID: 62230-10-8) in the PPD database retrieves comprehensive details across 16 key properties, including plant name, PPD-ID, compound/common name, CAS ID, molecular weight, InChI key, molecular formula, bioactivity reported for neurodegenerative diseases, canonical SMILES, biological source, synonyms, data source, chemical class, molecular mass, and docking score with the NLRP3 target protein, along with references (Figure 2).This phytochemical is of particular interest due to its documented neuroactive potential in various experimental studies [Beutler J. A., 2009]. Similarly, a search for "Harmaline" using its chemical name retrieves its complete profile, highlighting its neuroprotective role as a reversible monoamine oxidase A (MAO-A) inhibitor, which has been studied in the context of neurological disorders [Morales-García J. A. et al., 2017]. The compound summary includes all 16 properties, along with its docking score with the NLRP3 target protein, as illustrated in Figure S9.These search functionalities demonstrate the PPD database's efficiency in enabling rapid access to essential phytochemical information, supporting research into bioactive compounds targeting neurological disorders.The Psychoactive Plant Database (PPD) is a ground-breaking resource that connects modern drug discovery with traditional medicine. The database offers an extensive platform for investigating the therapeutic potential of psychoactive plants by listing more than 7,000 phytochemicals from 124 medicinal plants. It is an essential resource for researchers studying neurological and neurodegenerative disorders because it combines ethnopharmacological knowledge, computer analyses, and comprehensive phytochemical data.The medicinal potential of phytochemicals produced from plants is highlighted by important features such molecular docking investigations, phytochemical classification, and in silico ADMET profiling.In silico ADMET profiling, focused on CNS activity, of all the phytoconstituents was done which revealed that 29% of the compounds exhibited predicted CNS-active properties, with favorable pharmacokinetics supported by molecular weights below 500 g/mol. The database comprehends important information such as molecular weight, biological activity, biological source, class etc for all the collected phytoconstituents.Besides, all the literature reference of each phytoconstituent is also compiled and documented in the database. Additionally, all the phytoconstituents were also docked in the target NLRP3, which is a key target in neuroinflammation, and the binding affinity was also incorporated into the database for estimating the role of these phytoconstitutents in inhibiting the NLRP3 inflammasome. Similarity search of all these phytoconstituents was carried out on FDA approved drugs, the result of which could be useful for drug repurposing. Entire database is searchable by plant name, and also by using various key words such as molecular weight range, CAS ID, chemical name, biological activity, and more, ensuring ease of access for researchers.Overall, an attempt has been made to consolidate the entire traditional knowledge related to neurodegenerative diseases on a common single platform in order to accelerate the neurological drug discovery based on natural products. The PPD's emphasis on traditional knowledge ensures that valuable insights from indigenous and ancient practices are preserved and used in modern research. By facilitating the discovery of novel neuroprotective agents, PPD paves the way for safer and more effective treatments for neurological disorders, addressing an urgent global need.The database provides extensive information on all phytochemicals from known psychoactive plants, along with their physicochemical properties, reported bioactivity, predicted CNS properties, literature references, and predicted interactions with NLRP3, a key target for neuroinflammation. This comprehensive dataset in a single platform would be highly valuable for researchers working in the field of neurodegenerative diseases.The PPD will serve as a valuable tool for medicinal chemists, facilitating structure-activity relationship (SAR) studies across diverse phytochemical classes. This resource could also serve as a starting point for synthesizing new compounds based on selected scaffolds for neurodegenerative disease targets. Additionally, a network pharmacology approach can be applied to identify new potential targets, compounds, and explore synergistic interactions among various phytochemicals in the context of neurodegenerative diseases. along with the number of compounds identified in each plant Table S2. The total phytochemicals were classified using ClassyFire into 175 major chemical classes Table S3. Similarity search for database phytochemicals was performed against the drugbank small molecule library