1 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 (). While the concept is often used intuitively or colloquially, CS is inherently complex, and numerous formal definitions have been proposed and reviewed (). 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 (; ). 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 (). 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 (; ).
TABLE 1
| Type of data set, area covered | Exemplary data sets | Size range | Brief description |
|---|---|---|---|
| Drugs approved for clinical use | DrugBank () | FDA () | 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 () | 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 () | 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, 2025); PubChem (); CellMinerCDB () | ∼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 () | 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 () | 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 () CovalentInDB 2.0 () | 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 () | DLiP-PPI () 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 () | ∼14,000 | Macrocyclic lactones integrating structural and bioactivity data, designed to support cheminformatics analysis and predictive modeling of this compound class |
| Heterobifunctional degraders | PROTACs () | 10 | Manual compilation of representative PROTACs in clinical development |
| Pharmacogenomics | PharmGKB () | 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 () | LANaPDB () | 695,119 | 13,578 | Compilation of curated natural product databases |
| Food chemicals | FooDB () | >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 () | Compilation for FlavorMiner () | >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 () | OlfactionBase () | >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 () | OPCW schedules () | 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 |
Representative public compound data sets covering different regions of the BioReCS.a
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 (; ), for example, demand descriptors that strike a balance between computational efficiency and chemical relevance (). The rise of machine learning has led to the development of novel molecular representations (). 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 ().
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.
2 BioReCS
2.1 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 (), or proteolysis-targeting chimeras (PROTACs) (; ). 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 () or studying the overlap between flavor and odor chemicals (). 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.
2.2 Heavily explored chemical subspaces
In drug discovery, widely used public databases such as ChEMBL () and PubChem () 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 (; ; ). Importantly, to fully chart the boundaries of BioReCS, it is crucial to include negative biological data—that is, compounds known to lack bioactivity (; ). 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 (). Also, a recent development is the generation of InertDB, a compound collection with 3,205 curated inactive compounds obtained from PubChem (). 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 ().
2.3 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 (; ; ). 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 (; ; ) (Table 1). Despite their complexity, interest in characterizing these regions of chemical space is growing. Recent studies have addressed the CS of peptides (; ), agrochemicals (), metallodrugs (; ), macrocycles (; ), and PPIs (; ).
2.3.1 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 (; (). 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.
3 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.
3.1 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 () and the MAP4 fingerprint (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 (). However, there is still a pressing need to develop systematic molecular fingerprints for the study of biomaterials and inorganic molecules.
3.2 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 (), however, chemogenomic analyses on contemporary drugs (n = 3766) have shown that this percentage can reach 80% (). 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 (; ), including standard amino acid residues () to non-standard residues (). 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 (; ). 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.
3.3 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 (; ; ). 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 (). 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 (). 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.
3.4 Developing novel computational approaches
As the concept and application of chemical space evolve, so too must the computational tools used to explore it (). 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 (). Looking ahead, we anticipate increasing use of hybrid computational workflows, which combine descriptor-based, rule-based, and AI-driven methods (). In parallel, new methods for analyzing multiple dimensions and types of information—such as chemical multiverse analysis and the creation of consensus chemical spaces (; ; ) —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.
4 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.
Statements
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.
Funding
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.
Acknowledgments
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.
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Correction note
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Abbreviations
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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Summary
Keywords
chemoinformatics, dark chemical matter, de novo design, food chemicals, metallodrugs, natural products, odor chemicals, peptides
Citation
Medina-Franco JL, López-López E, Avellaneda-Tamayo JF and Zamora WJ (2025) On the biologically relevant chemical space: BioReCS. Front. Drug Discov. 5:1674289. doi: 10.3389/fddsv.2025.1674289
Received
27 July 2025
Accepted
12 August 2025
Published
25 August 2025
Corrected
05 December 2025
Volume
5 - 2025
Edited by
Rodolpho C. Braga, InsilicAll, Brazil
Updates
Copyright
© 2025 Medina-Franco, López-López, Avellaneda-Tamayo and Zamora.
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*Correspondence: José L. Medina-Franco, medinajl@unam.mx
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