Front. Drug Discov. Frontiers in Drug Discovery Front. Drug Discov. 2674-0338 Frontiers Media S.A. 1674289 10.3389/fddsv.2025.1674289 Opinion On the biologically relevant chemical space: BioReCS Medina-Franco et al. 10.3389/fddsv.2025.1674289 Medina-Franco José L. 1 * Conceptualization Funding acquisition Resources Writing – review & editing Writing – original draft Project administration Supervision Formal analysis López-López Edgar 1 2 Formal analysis Investigation Writing – review & editing Avellaneda-Tamayo Juan F. 1 Formal analysis Investigation Writing – review & editing Zamora William J. 3 4 Funding acquisition Formal analysis Writing – review & editing Investigation DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City, Mexico Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Section 14-740, Mexico City, Mexico CBio3 Laboratory, School of Chemistry, University of Costa Rica, San José, Costa Rica Laboratory of Computational Toxicology and Biological Testing Laboratory (LEBi), University of Costa Rica, San José, Costa Rica Correspondence: José L. Medina-Franco, medinajl@unam.mx 25 08 2025 05 12 2025 2025 5 1674289 27 07 2025 12 08 2025 Copyright © 2025 Medina-Franco, López-López, Avellaneda-Tamayo and Zamora. 2025 Medina-Franco, López-López, Avellaneda-Tamayo and Zamora https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. chemoinformatics dark chemical matter de novo design food chemicals metallodrugs natural products odor chemicals peptides Universidad Nacional Autónoma de México 10.13039/501100005739 LANCAD-UNAM-DGTIC-335 Universidad de Costa Rica 10.13039/501100005298 The author(s) declare that financial support was received for the research and/or publication of this article. We thank the Dirección General de Cómputo y de Tecnologías de la Información y Comunicación (DGTIC), Universidad Nacional Autónoma de México, for the computational resources to use Miztli under project LANCAD-UNAM-DGTIC-335. WZR thanks the Vice Chancellor for Research of the University of Costa Rica for its support via the research project 908-C3-610. section-in-acceptance In silico Methods and Artificial Intelligence for Drug Discovery Introduction

The “chemical space” (CS), “chemical compound space,” or “chemical universe” terms are frequently used in drug discovery and other areas, including chemical synthesis, catalysis, materials science, food chemistry, and agrochemistry, among others (Kim et al., 2024). While the concept is often used intuitively or colloquially, CS is inherently complex, and numerous formal definitions have been proposed and reviewed (Medina-Franco et al., 2022). A commonly accepted notion of CS relates to the number of chemical compounds that could theoretically exist—the “size” of chemical space—which varies greatly depending on the classes of compounds considered (e.g., small organic molecules, peptides, odorants). Another perspective views CS as a multidimensional space in which molecular properties (both structural and functional) define coordinates and relationships between compounds (Virshup et al., 2013; Martinez-Mayorga and Medina-Franco, 2014). These definitions give rise to the concept of chemical subspaces (ChemSpas): subsets of the broader chemical universe distinguished by shared structural or functional features. Within this framework, the biologically relevant chemical space (BioReCS) comprises molecules with biological activity—both beneficial and detrimental. BioReCS spans diverse application areas such as drug discovery, agrochemistry, sensory chemistry (e.g., flavor and odor), food science, and natural product research. It also includes compounds with reactive molecules, including promiscuous and poly-active molecules, as well as those with highly detrimental or undesirable effects, such as toxic and allergic compounds.

Chemical compound databases are key resources for exploring the CS and are central to chemoinformatics (Williams and Richard, 2025). Numerous public databases—varying in size and specialization—target specific regions of BioReCS. Table 1 provides representative examples of freely available libraries across several domains. Comprehensive reviews of chemoinformatic and bioinformatic databases have been published elsewhere (Rigden and Fernández, 2025; de Azevedo et al., 2024).

Representative public compound data sets covering different regions of the BioReCS. a

Type of data set, area covered Exemplary data sets Size range Brief description
Drugs approved for clinical use DrugBank (Knox et al., 2023) | FDA (Center for Drug Evaluation and Research, 2025) 17,481 entries | 4,563 approved chemical entities Comprehensive, manually curated resource integrating detailed drug, drug–target, and pharmacological data. The FDA set is included in DrugBank
Metallodrugs MetAP DB (López López and Medina-Franco, 2025) 61 Metal-based approved drug database. Compounds are classified according to their clinical uses: metallodrug, imaging, radioimaging, radiotherapy, and photodynamic
Compounds and tools for drug repositioning DrugRepoBank (Huang et al., 2024) Bioactive compounds: 49,652; Drug–target interactions: 880,945; Drug–disease associations: 28,978; Drug–side effect associations: 109,698; Target proteins: 4,221; Drug gene-expression signatures: 473,647 A comprehensive, curated database and discovery platform designed to accelerate drug repositioning
Compounds in clinical trials ClinicalTrials (ClinicalTrials.gov, 2025) ≈530,000 entries Database of clinical research studies and information about their results. Generated by the U.S. National Institutes of Health and other U.S. agencies. Data on clinical entries from 200 countries
Compounds annotated with biological activity ChEMBL (Zdrazil et al., 2023; Zdrazil, 2025); PubChem (Kim et al., 2024); CellMinerCDB (Shankavaram et al., 2009) ∼2.4 M | > 322 M | >20,000 compounds Repositories of biologically annotated compounds, integrating experimental bioactivity data, clinical-phase molecules, drug repurposing candidates, and chemical probe information. | CellMiner Integrates genomic and pharmacologic data for the NCI-60 panel of 60 diverse human cancer cell lines, representing 9 different cancer types
Peptides Peptipedia v2.0 (Cabas-Mora et al., 2024) 3,983,654 sequences; 103,561 active labeled Largest bioactive peptide compilation database to 2024, with more than 200 bioactivity types. Web-based tools include secondary structure evaluation, functional domain analysis, physicochemical, and thermodynamic properties
Proteomics ProteomicsDB (Schmidt et al., 2017) Number of LC-MS/MS experiments: ∼19,000; Human tissues/body fluids: ∼41; Cell line datasets: ∼60 Protein-centric database designed for exploration of large-scale quantitative mass spectrometry proteomics data. Multi-omics data types: transcriptomics, proteomics, functional drug-sensitivity, and interaction networks
Targeted covalent inhibitors (TCIs) CovBinderInPDB (Guo and Zhang, 2022)CovalentInDB 2.0 (Du et al., 2024) 7,375 covalent modifications; 8,303 inhibitors Curated databases to support the design of TCIs. Covalent interactions detailing binders across diverse residues. Expand on bioactivity data, target profiles, ligandability predictions, and libraries of commercial and natural product-derived covalent compounds
Protein-protein interaction (PPI) inhibitors iPPI-DB (Torchet et al., 2021) | DLiP-PPI (Ikeda et al., 2023) ref 2,374 compounds | 32,647 PPI-related compounds Manually curated, community-extendable resource featuring annotated PPI modulators and stabilizers | Newly synthesized and literature-extracted molecules, characterized by properties tailored for PPI inhibition, along with target-specific filtering, and activity data
Macrocycles MacrolactoneDB (Zin et al., 2020) ∼14,000 Macrocyclic lactones integrating structural and bioactivity data, designed to support cheminformatics analysis and predictive modeling of this compound class
Heterobifunctional degraders PROTACs (Srivastava et al., 2025) 10 Manual compilation of representative PROTACs in clinical development
Pharmacogenomics PharmGKB (Gong et al., 2021) Drugs: 715; Genes: 1,761; Diseases/phenotypes: 227; Clinical dosing guidelines: 165; Drug labels annotated: 784; Variant annotations: >5,000 individual variant–drug summaries It specializes in curated information about how human genetic variation affects drug response—covering clinical dosing guidelines, drug label annotations, variant–drug associations, and gene–pathway data to support both research and clinical precision medicine
Natural product compounds COCONUT (Chandrasekhar et al., 2024) | LANaPDB (Gómez-García et al., 2024) 695,119 | 13,578 Compilation of curated natural product databases
Food chemicals FooDB (Harrington et al., 2019) >3 M records and observations, corresponding to 128,283 different foods Database focused on the chemical composition of foods and their associated health effects
Flavor molecules Kou et al. compilation (Kou et al., 2023) | Compilation for FlavorMiner (Herrera-Rocha et al., 2024) >14,000 unique flavor molecules (8982 molecules with known taste and 5,046 with known aroma) | 13,387 compounds Compilation of 25 flavor molecule databases published within the last 20 years | Compilation of molecules with experimentally validated flavor profiles
Odor chemical Pyrfume (Hamel et al., 2024) | OlfactionBase (Sharma et al., 2021) >20,000 odorants | 2,871 entries related to odorant/pheromone binding Unified dataset of stimulus-linked olfactory datasets | Includes odors, odorants, and odorless compounds and their interactions with different receptors
Toxic chemicals TOXNET (Davis et al., 2020) | OPCW schedules (Annex on Chemicals, 2025) 103,062,149 toxicogenomic data, including chemical–gene/protein interactions, chemical–disease and gene–disease relationships | >35,000 chemical weapons A publicly available database that aims to advance understanding about how environmental exposures affect human health| Substances are organized into two categories: Toxic and precursors

The list of compound databases is not exhaustive. Exemplary databases are shown.

A systematic study of CS requires molecular descriptors that define the dimensionality of the space. The choice of descriptors depends on project goals, compound classes (e.g., metal-containing vs purely organic molecules), and the dataset size and diversity. Large and ultra-large chemical libraries that are highly used today in drug discovery projects (Lyu et al., 2019; Corrêa Veríssimo et al., 2024), for example, demand descriptors that strike a balance between computational efficiency and chemical relevance (Warr et al., 2022). The rise of machine learning has led to the development of novel molecular representations (Wigh et al., 2022). Visualization is another critical tool for CS analysis, because these spaces often involve many dimensions; dimensionality-reduction techniques are commonly used to project them into two or three dimensions for interpretation. Recent reviews detail advancements in the visualization of chemical space (Sosnin, 2025).

In this article, we offer an integrative perspective on BioReCS, highlighting common considerations for its consistent and meaningful exploration. We also address its size, historical evolution, and future expansion.

 BioReCS Current view

In many research projects, the chemical universe—and by extension, BioReCS—is explored through distinct sections of chemical subspaces (ChemSpas). For instance, CS analyses may focus specifically on small-molecule drug candidates, peptides (Orsi and Reymond, 2024), or proteolysis-targeting chimeras (PROTACs) (Danishuddin et al., 2023; Sincere et al., 2023). Other studies target agrochemicals, odorants, natural products, or metal-containing compounds. Some research initiatives are at the intersection of multiple ChemSpas, such as investigating bioactive compounds that straddle both natural product and food chemical domains (Avellaneda-Tamayo et al., 2024) or studying the overlap between flavor and odor chemicals (Cui et al., 2025). Analyzing these intersecting regions of chemical space often requires integrating methodologies from diverse disciplines. In this section, we highlight both heavily explored and underexplored regions of BioReCS.

 Heavily explored chemical subspaces

In drug discovery, widely used public databases such as ChEMBL (Zdrazil, 2025) and PubChem (Kim et al., 2024) serve as major sources of biologically active small molecules, primarily organic compounds. Owing to their extensive biological activity annotations, these databases are major sources of poly-active compounds and promiscuous structures. Table 1 summarizes these and other key databases that cover different regions of BioReCS. The chemical space of drug-like molecules, particularly small organic compounds and natural products, has been extensively studied. Closely related areas, such as small peptides and other beyond Rule of 5 (bRo5) entities, are also well-characterized using computational approaches (Price et al., 2024; Capecchi and Reymond, 2021; López-López et al., 2023). Importantly, to fully chart the boundaries of BioReCS, it is crucial to include negative biological data—that is, compounds known to lack bioactivity (Williams et al., 2016; López-López et al., 2022). These data help define the non-biologically relevant portions of chemical space. A notable example is dark chemical matter, a large-scale dataset comprising small molecules from corporate compound collections that have repeatedly failed to show activity in high-throughput screening assays (Wassermann et al., 2015). Also, a recent development is the generation of InertDB, a compound collection with 3,205 curated inactive compounds obtained from PubChem (An et al., 2025). The database also includes 64,368 putative inactive molecules generated with a deep generative artificial intelligence (AI) model trained on the experimentally determined inactive molecules (An et al., 2025).

 Underexplored chemical subspaces

Certain types of chemical structures remain underrepresented in chemoinformatics due to modeling challenges. A prominent example is metal-containing molecules, which are often excluded during data curation because most chemoinformatics tools are optimized for small organic compounds (Fourches et al., 2016; Bento et al., 2020; Valle-Núñez et al., 2025). Metallodrugs, therefore, represent a structurally and functionally important class that is commonly filtered out by default. However, the difficulty of modeling a region of BioReCS should not justify its exclusion. Similarly, various compound classes are rarely targeted in drug discovery efforts, including large and complex natural products, macrocycles (compounds containing rings of ≥12 atoms), protein-protein interaction (PPI) modulators or inhibitors, PROTACs, and mid-sized peptides. Many of these molecules fall into the beyond Rule of 5 (bRo5) category (Price et al., 2024; Whitty and Zhou, 2015; Schaub et al., 2021) (Table 1). Despite their complexity, interest in characterizing these regions of chemical space is growing. Recent studies have addressed the CS of peptides (Orsi and Reymond, 2024; Capecchi et al., 2019), agrochemicals (Zhang et al., 2018), metallodrugs (Meggers, 2007; López López and Medina-Franco, 2025), macrocycles (Viarengo-Baker et al., 2021; Kim et al., 2025), and PPIs (Zhang et al., 2014; Choi et al., 2021).

Dark regions of the underexplored BioReCS

Beyond beneficial regions, BioReCS also encompasses gray-to-dark areas—zones that include compounds with undesirable biological effects, such as toxic chemicals (Tihányi et al., 2025; (Annex on Chemicals, 2025). Understandably, these regions have received less attention than areas linked to therapeutic or beneficial activity. Nonetheless, distinguishing the characteristics that separate harmful compounds from beneficial ones is vital for the design of safer, human-beneficial, and ecologically responsible molecules.

 Common considerations to explore BioReCS

In this section, we highlight common challenges associated with exploring BioReCS, along with possible workarounds and emerging directions. While not exhaustive, these topics are meant to illustrate recurring issues and encourage a holistic consideration of the BioReCS.

Towards universal descriptors

The structural diversity across underexplored regions of BioReCS presents a major challenge to define a consistent chemical space using molecular descriptors. Traditional descriptors, tailored to specific ChemSpas such as small molecules, peptides, or metallodrugs, lack universality. However, there are ongoing efforts to develop structure-inclusive, general-purpose descriptors. Notable examples include molecular quantum numbers (Nguyen et al., 2009) and the MAP4 fingerprint (Capecchi et al., 2020 ref), which is designed to accommodate entities ranging from small molecules to biomolecules and even metabolomic data. More recently, neural network embeddings derived from chemical language models have shown promise in encoding chemically meaningful representations that can reconstruct molecular structures or predict properties (Lžičař and Gamouh, 2024). However, there is still a pressing need to develop systematic molecular fingerprints for the study of biomaterials and inorganic molecules.

 pH-dependent chemical space

Many bioactive compounds, especially drugs, are weak bases, acids, or ampholytes that can ionize depending on the pH of their environment. Pioneering studies have reported that 62.9% of compounds in the World Drug Index (n = 582) are ionizable, with the majority being bases, fewer acids, and some ampholytes (Manallack, 2007), however, chemogenomic analyses on contemporary drugs (n = 3766) have shown that this percentage can reach 80% (Manallack et al., 2013). In consequence, the ionization state—charged or neutral—of a bioactive compound profoundly impacts its solubility, permeability, absorption, distribution, toxicity, and binding, making this distinction essential in drug development and computational modeling. However, CS analyses typically assume molecular structures with neutral charge, which may not reflect the actual bioactive species of compounds under physiological or environmental conditions. Even when the structural representation of an ionizable compound is accurate, chemoinformatics tools often calculate molecular descriptors such as lipophilicity (logP) based solely on the neutral species, overlooking the dominant ionic forms. Computing lipophilicity using logD at physiological pH is much more relevant than using logP for small molecules (Bhal et al., 2007; Zamora et al., 2017), including standard amino acid residues (Zamora et al., 2019) to non-standard residues (Viayna et al., 2024). Those limitations underscore the need for implementing chemoinformatics tools capable of calculating molecular properties contingent on the ionization state of bioactive compounds as a function of environmental pH in CS research (Bertsch et al., 2023; Bertsch-Aguilar et al., 2024). This highlights that neglecting the pH-dependent behavior of bioactive compounds could limit the biological relevance of BioReCS. Consequently, future efforts should aim to incorporate protonation state dynamics to enhance their representativeness in pH-dependent CS analysis.

 De novo generated libraries: expanding the BioReCS

In drug discovery and beyond, there is growing interest in creating on-demand, synthetically accessible virtual libraries for high-throughput screening (Perebyinis and Rognan, 2022; Grygorenko et al., 2020; Chávez-Hernández et al., 2023). Advances in generative models have accelerated the enumeration of the large and ultra-large chemical libraries, expanding the known chemical space and enabling the design of extensive libraries guided by structure or property constraints (Ye, 2024). However, evaluating the usefulness of such libraries requires more than sheer size; chemical diversity, as assessed through fingerprints, scaffolds, and physicochemical descriptors, is equally critical. Notably, a recent historical analysis of ChEMBL, PubChem, and DrugBank revealed that newer libraries are not necessarily more diverse (Lopez Perez et al., 2025). A similar trend could be observed for the continuously enumerated ultra-large chemical libraries, highlighting the need to quantify their chemical diversity using multiple structural representations. For BioReCS, we must consider not only the scale and diversity of expansion but also its direction—whether new molecules occupy unexplored regions or merely populate existing subspaces. Depending on the application area (e.g., drug discovery), the bioactivity profile should also be considered to avoid populating regions of BioReCS with promiscuous compounds associated with undesirable clinical effects.

 Developing novel computational approaches

As the concept and application of chemical space evolve, so too must the computational tools used to explore it (Reymond, 2025). Novel or less conventional regions of drug-like space, such as bRo5 compounds discussed in Section 2.2, demand innovative methodologies or adaptations of existing ones. For instance, a recently developed hybrid fingerprint was designed specifically to accommodate metal-containing molecules, extending traditional organic-focused fingerprints by incorporating metal-specific features (López López and Medina-Franco, 2025). Looking ahead, we anticipate increasing use of hybrid computational workflows, which combine descriptor-based, rule-based, and AI-driven methods (Medina-Franco et al., 2024). In parallel, new methods for analyzing multiple dimensions and types of information—such as chemical multiverse analysis and the creation of consensus chemical spaces (Medina-Franco et al., 2022; Medina-Franco et al., 2019; López-López and Medina-Franco, 2023) —will enable more efficient use and integration of available data. Finally, machine learning models trained in known regions of BioReCS will play a pivotal role in navigating uncharted subspaces and improving coverage of BioReCS.

 Discussion

In this opinion article, we offered a holistic perspective on the biologically relevant chemical space (BioReCS) as a subset of the broader chemical universe. Effective navigation of BioReCS requires not only cataloging active compounds but also systematically reporting biologically inactive molecules, which help define the limits of relevance. While most of the explored regions focus on human-beneficial activities—such as therapeutic development, agriculture, and food sciences—BioReCS also includes dark regions populated by undesirable or toxic compounds. Recognizing and learning from these contrasts is essential for safer, ecologically responsible, and more targeted molecular design. The exploration of understudied ChemSpas may drive the development or refinement of computational tools, especially in cases where current methods fall short. Broadening the scope of BioReCS analysis—from both a structural and functional standpoint—could reveal hidden subspaces containing compounds with novel or unexpected biological activities. Importantly, training machine learning models on known BioReCS data will enhance our capacity to identify uncharted regions and optimize exploration strategies. As chemical databases continue to grow, it is important to emphasize that expansion alone does not equate to increased chemical diversity or biological relevance. Future research should consider not only the scale of these libraries but also their directionality, structural diversity, and applicability to real-world biological contexts.

 Author contributions

JM-F: Conceptualization, Funding acquisition, Resources, Writing – review and editing, Writing – original draft, Project administration, Supervision, Formal Analysis. EL-L: Formal Analysis, Investigation, Writing – review and editing. JA-T: Formal Analysis, Investigation, Writing – review and editing. WZ: Funding acquisition, Formal Analysis, Writing – review and editing, Investigation.

 Acknowledgements

Insights and rich discussions with Karina Martinez-Mayorga and Gerald M. Maggiora are highly acknowledged. EL-L and JFA-T thank the Consejo Nacional de Humanidades, Ciencias y Tecnología (CONAHCyT) for the PhD scholarships 762342 (No. CVU: 894234), and 1270553, respectively.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

 Correction note

This article has been corrected with minor changes. These changes do not impact the scientific content of the article.

 Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Edited by: Rodolpho C. Braga, InsilicAll, Brazil

Reviewed by: Ho Leung Ng, Atomwise Inc, United States

Andrea Trabocchi, University of Florence, Italy

AI, artificial intelligence; bRo5, beyond Rule of 5; ChemSpa, chemical subspace; CS, chemical space; BioReCS, biological-relevant chemical space; PROTACs, proteolysis-targeting chimeras; PPI, protein-protein interaction.

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