Artificial intelligence for small-molecule chemical probe design: why drug-centric models may not transfer cleanly

1 Introduction

Small-molecule chemical probes are foundational tools for target validation, mechanistic interrogation, and early decision-making in drug discovery (Porta and Steel, 2023). They comprise a diverse set of fit-for-purpose compounds, ranging from unlabeled reversible or covalent ligands used to perturb target function to modality-specific constructs, such as fluorescent or activity-based probes (ABPs), as well as broader classes of chemical proteomics probes used in proteome-wide interaction studies (Tan et al., 2025). Accordingly, they are not defined by a single structural architecture but by their ability to support reliable biological inference under defined experimental conditions. Chemical probe development cannot therefore be reduced to the optimization of drug-like properties alone. In addition to sufficient potency, successful probes must provide selectivity, support reliable target engagement and/or target perturbation in the relevant biological context, and enable mechanistically interpretable readouts under defined conditions of use.

Seminal guidance on probe quality emphasized “fitness factors” spanning chemical properties, biological potency, biological selectivity, and context of use, warning that poor tools and practices can misdirect both basic biology and translational research (Workman and Collins, 2010). Subsequent consensus literature reinforced both the promise and risk of chemical probes: although they enable rapid functional interrogation of targets, low-quality or misapplied compounds can generate persistent false narratives in the literature and distort downstream discovery efforts (Arrowsmith et al., 2015). This challenge extends to probe controls themselves. Negative controls are often treated as straightforward safeguards; however, careful analysis shows that an inactive probe can also alter off-target profiles or physicochemical behavior, making control design a non-trivial medicinal chemistry problem rather than a routine afterthought (Lee and Schapira, 2021).

In parallel, artificial intelligence (AI) has become an increasingly influential component of small-molecule design. This ecosystem spans benchmark suites for de novo design and goal-directed optimization, such as GuacaMol and MOSES, as well as platform-based implementations, such as Chemistry42, that integrate generative models with multi-parameter scoring pipelines (Brown et al., 2019; Polykovskiy et al., 2020; Ivanenkov et al., 2023). Nevertheless, the dominant assumptions embedded in this ecosystem remain overwhelmingly drug-centric. Most workflows are optimized around endpoints such as potency, ADMET, drug-likeness, and synthetic accessibility, and are assessed using surrogate objective functions that do not fully capture the evidentiary demands of probe science. Meanwhile, the generative modeling literature has demonstrated that reward hacking, model brittleness, and out-of-domain extrapolation can produce compounds that appear compelling in silico but do not withstand experimental scrutiny (Langevin et al., 2022; Renz et al., 2019). In drug discovery, such failures may waste time or resources; in chemical probe discovery, they can be more damaging by misleading mechanistic interpretation and confounding target-validation efforts.

In this Perspective, we argue that chemical probes expose a sharper version of the general AI-in-drug-discovery problem: the optimization target is different, the data are thinner and more heterogeneous, and the cost of failure is not only poor prioritization but also misleading biological inference. We therefore examine how current AI methods can contribute to probe discovery, where they have already shown promise, and why drug-centric models may not transfer cleanly. Particular attention is given to fluorescent and covalent probe modalities, where modality-specific requirements such as photophysical behavior, intrinsic reactivity, and site selectivity make the mismatch especially visible. This conceptual distinction is summarized in Figure 1, which contrasts conventional drug-centric AI workflows with a probe-centric framework centered on evidence generation, target validation, and context of use.

2 Chemical probe design is a distinct AI problem

2.1 Chemical probes versus drug candidates

Although chemical probes and drug candidates may share targets and, in some cases, core binding scaffolds, they are optimized for different purposes. For some probe classes, the relevant design space extends beyond target binding to include modality-specific functionality, such as electrophilic warheads, linkers, or reporter groups. Consequently, AI-guided chemical probe design should be treated as a distinct discovery problem rather than a reduced version of AI-guided drug design. The key distinction is that chemical probes and drug candidates are evaluated according to different success criteria. Drug candidates must ultimately satisfy exposure, safety, and efficacy constraints in humans; chemical probes must enable confident causal inference about target function under defined experimental conditions (Vu et al., 2022). In practice, this changes the evidentiary emphasis in several ways. First, selectivity is often a non-negotiable probe requirement because observed phenotypes must be attributable to a defined target perturbation. Probe frameworks therefore emphasize not only biochemical potency but also broad selectivity assessment and orthogonal validation strategies (Mader et al., 2023; Porta, 2023). Second, although both probes and drug candidates benefit from evidence of target engagement in relevant cellular contexts, for probes this evidence serves a more immediate mechanistic purpose: it must support the attribution of the observed phenotype to an on-target perturbation in the specific experimental system (Porta et al., 2026). Accordingly, probe evaluation places particular weight on demonstrating engagement and functional perturbation under assay-relevant conditions, where permeability, efflux, binding competition, and local cellular context can decouple biochemical potency from biological effect (Vu et al., 2022; Mader et al., 2023; Isern et al., 2025). Third, probes ideally include control compounds (negative controls and/or orthogonal chemotypes) to distinguish on-target from off-target biology. However, careful analysis shows that negative controls can themselves be misleading when structural inactivation introduces new off-targets or alters physicochemical behavior, underscoring that control design is itself a medicinal chemistry problem (Lee and Schapira, 2021).

Critically, evidence indicates that these standards are not consistently applied across the literature. A systematic analysis of probe use has found that recommended concentration windows and appropriate controls are frequently not followed, even for probes curated in expert resources, suggesting that the interpretability of probe-derived results is compromised when the context of use is not incorporated into routine experimental practice (Sterling et al., 2023; Sanfelice et al., 2025). From an AI perspective, this reframes the learning problem: not simply toward optimizing compounds against generic multi-parameter objectives, but toward prioritizing molecules against probe-specific, context-dependent, and functionally interpretable criteria, together with the evidence package and conditions of use required for reliable biological inference. This distinction defines the logic of the discussion that follows: we next examine which current AI capabilities map most meaningfully onto these probe-specific requirements, then consider where probe-relevant evidence already exists, and finally discuss the data, validation, and tractability barriers that still limit broader transfer.

2.2 AI methods relevant to chemical probe design

For chemical probes, the key issue is not whether AI methods can optimize molecular properties in general, but whether they can support the specific evidentiary requirements that distinguish probes from drug candidates, including selectivity, target engagement, control design, and context-of-use constraints. For clarity, we discuss these methods through three overlapping functional lenses: predictive modeling, generative and optimization workflows, and design-to-experiment frameworks that connect molecular design to feasible synthesis and experimentally testable hypotheses. These are not mutually exclusive categories, and many current platforms integrate elements of more than one.

Within this Perspective, we use “relevance” in a probe-centric rather than a generic drug-design sense. An AI method is relevant to chemical probe discovery only insofar as it can help model, prioritize, or experimentally de-risk one or more determinants of probe quality:

• Selectivity and off-target liability.
• Target engagement in the relevant biological context.
• The feasibility of matched negative controls or orthogonal chemotypes, that is, companion compounds that help distinguish on-target from off-target biology without introducing new interpretive confounders.
• Compatibility with assay context and intended conditions of use.
• Synthetic and derivatization tractability, particularly when tagged or modality-specific probe variants are required.

Methods that optimize potency, similarity, or generic drug-likeness without informing these probe-specific dimensions may still be useful for early molecular triage, but they do not by themselves provide strong support for chemical probe discovery.

Among the three lenses, predictive modeling is currently the most directly relevant where probe design depends on estimating selectivity, prioritizing off-target risk, or identifying the boundaries of reliable extrapolation. Predictive modeling remains the foundation of most AI-enabled molecular design workflows and still depends heavily on curated bioactivity and property datasets. Standard molecular machine learning (ML) benchmarks, including MoleculeNet, have accelerated the development of representations and algorithms for physicochemical and bioactivity prediction, whereas comparative studies have established message-passing neural networks as strong default predictors for many endpoints (Wu et al., 2018; Yang et al., 2019). However, for chemical probes the most relevant endpoints are often not single-target potency measures but multi-target and multi-modal properties, particularly selectivity. Proteochemometrics offers one route toward this problem by combining ligand and protein descriptors to predict activity across protein families, thereby providing a computational framework for modeling selectivity rather than isolated potency values (Bongers et al., 2019). In the probe context, this is relevant because selectivity across related targets is central to distinguishing interpretable on-target perturbation from confounding off-target effects. However, its practical value still depends on dense cross-target measurements, consistent assay formats, and careful prospective validation.

Uncertainty quantification and generalization are especially important in the probe setting, where misleading confidence can distort mechanistic interpretation rather than simply deprioritizing a compound series. Conformal prediction, a statistical framework for estimating prediction confidence and defining the model’s applicability domain more explicitly (i.e., the range of chemical problems for which its predictions can be treated as reliable) within quantitative structure-activity relationship (QSAR), is particularly relevant when out-of-distribution extrapolation may produce spurious high-confidence predictions, not as an abstract modeling refinement, but as a practical way to limit overconfident extrapolation in probe-relevant settings (Bosc et al., 2019). This concern is reinforced by examples from the goal-directed generative-design literature, in which optimization can exploit model artifacts and produce molecules that appear successful in silico while failing to support reliable mechanistic inference experimentally (Renz et al., 2019; Langevin et al., 2022).

Generative and optimization workflows become relevant to probe discovery only when the optimization target extends beyond potency and generic developability to include probe-specific evidence requirements. Generative molecular design platforms and benchmark ecosystems emphasize the exploration of chemical space and the optimization of multi-parameter objective functions (Kang et al., 2025; Van Den Broek et al., 2025). Frameworks such as MolScore simplified the assembly of scoring functions and the evaluation of generative performance; however, the objectives most commonly used in these pipelines remain predominantly drug-centric, including similarity, docking, predicted ADMET, and synthesizability (Thomas et al., 2024). In their standard form, these pipelines do not readily encode probe-specific requirements such as matched inactive controls, broad selectivity matrices, or evidence of target engagement. This is not because such properties are intrinsically unmodelable, but because they are rarely available as standardized, scalable, and probe-relevant labels in the datasets used to train and score current workflows, and because some are inherently context-dependent or relational rather than single-molecule endpoints. A probe-oriented generative framework would, therefore, need to incorporate additional objective terms and constraints, together with data representations and annotation schemes that better capture these probe-specific requirements. Probe-oriented workflows may ultimately require the serial or parallel integration of multiple predictive components, with distinct models or scoring terms for selectivity, target engagement, assay-context compatibility, and tractability (Loeffler et al., 2024). However, the limiting factor is not only the workflow architecture but also the availability of robust probe-relevant labels for each of these objectives. In other words, the current limitation is not simply that generative models are imperfect, but that most are still optimized against objective functions that do not reflect what makes a molecule informative and trustworthy as a chemical probe.

Finally, design-to-experiment frameworks become relevant to probe discovery when they couple computational prioritization to experimentally measurable endpoints. In the DNA-encoded libraries (DELs) with ML setting, the primary inputs are large experimental selection datasets generated from encoded compound libraries together with molecular representations used for model training (Iqbal et al., 2025; McCloskey et al., 2020). The resulting models are used to predict enrichment, prioritize compounds for synthesis or follow-up, and refine which regions of library space are most worth pursuing experimentally. For chemical probes, however, the value of this workflow lies not simply in producing binders, but in accelerating entry into probe-relevant validation cascades, where selectivity profiling, cellular target engagement, and control-compound strategy determine whether a prioritized hit can mature into a credible probe. A related future direction is human-in-the-loop reward elicitation, in which the primary inputs are not only molecular descriptors or model outputs, but also structured expert judgments about what constitutes a promising design. In this setting, expert feedback is used to refine scoring functions or optimization objectives during molecular generation (Menke et al., 2024; Sundin et al., 2022). Its potential relevance to chemical probes lies in the fact that several probe-defining criteria, such as acceptable selectivity trade-offs, control-compound plausibility, or assay-context suitability, are often difficult to encode from public datasets alone and may initially require expert-guided prioritization. At present, however, this remains largely prospective for probe discovery and will only become practically useful if coupled to probe-relevant readouts and validation frameworks.

Taken together, these three functional lenses are not equally informative for all probe-design objectives. Predictive models are currently most mature for selectivity-related inference and uncertainty-aware triage, whereas generative and optimization workflows become most useful only when probe-relevant objectives can be encoded explicitly rather than inherited from drug-centric scoring schemes. Design-to-experiment frameworks are most valuable when computational suggestions can be linked directly to experimentally measurable probe endpoints. Accordingly, the strongest current evidence for AI-guided chemical probe discovery comes from application domains in which probe-relevant criteria can already be operationalized, measured, and iteratively validated, particularly fluorescent and covalent probe modalities.

2.3 Current applications of AI in chemical probe discovery

Despite growing enthusiasm, published examples of AI workflows explicitly framed as chemical probe design remain comparatively sparse. A recent example is the integration of quantitative high-throughput screening with machine learning and pharmacophore modeling to identify isoform-selective aldehyde dehydrogenase chemical probe candidates, including compounds supported by cellular target-engagement assays, illustrating that AI-guided probe discovery is feasible when selectivity and validation are built into the workflow (Yasgar et al., 2025). Accordingly, the strongest current evidence comes from probe-selection infrastructures and modality-specific applications in which probe-relevant endpoints differ sharply from classical drug-discovery objectives and can therefore be evaluated more explicitly. For example, fluorescent and enzyme-imaging probes may need to optimize signal generation, fluorogenic activation, reporter detectability under biologically relevant assay conditions, or substrate-activation specificity, whereas drug-candidate workflows typically prioritize potency, ADMET, exposure, and developability (Nguyen et al., 2025). Among these, the clearest current examples come from fluorescent and covalent probe modalities, where key performance attributes are unusually well defined and experimentally measurable.

Probe-oriented resources make probe quality and the context of use more explicit, even when they are not themselves generative AI systems. Probe Miner operationalized data-driven scoring of chemical probes using public medicinal chemistry data, enabling quantitative ranking while simultaneously exposing the incompleteness and bias of public datasets (Antolin et al., 2018). The Chemical Probes Portal complements this approach through expert review, ratings, and best-practice guidance (Antolin et al., 2023), and its continued development underscores both the growing demand for probe decision support and the persistence of suboptimal probe use in the literature (Sanfelice et al., 2025). These resources are therefore best understood as probe-centric decision infrastructures upon which future AI systems could be built, rather than as molecular design engines. Their value in this context is therefore not that they already solve probe design, but that they begin to formalize the decision logic that future probe-oriented AI systems would need to learn or support.

These methodological considerations become most informative in application domains where probe-relevant endpoints can be measured, aggregated, and linked back to design decisions. As mentioned above, fluorescent probe discovery has produced some of the clearest AI-forward exemplars because photophysical properties can be measured at scale and are poorly represented in conventional drug-design datasets. In this setting, probe performance depends not only on favorable optical properties in isolation but also on whether the reporter signal remains detectable under biologically relevant assay conditions. In target-specific fluorescent probes, these design variables are often interdependent because optimization of the fluorophore or linker can alter target affinity, cellular behavior, or signal output rather than leaving target recognition unchanged (Kok et al., 2022; Baker et al., 2010). Large experimental datasets have enabled ML models for emission wavelength and quantum yield, including solvent-dependent effects (Ju et al., 2021), while broader deep-learning frameworks have shown that multiple optical descriptors can be predicted in parallel to support screening and molecular design (Joung et al., 2021). More directly relevant to probe discovery, NIRFluor provides a deep-learning platform for screening near-infrared fluorophores against desired optical properties (Wang et al., 2025), and PROBY integrates AI with bioassays to accelerate the discovery of target-specific fluorescent probes (Jiang et al., 2026). Together, these studies suggest that AI can meaningfully contribute to probe design when endpoints are well defined, datasets can be aggregated at scale, and computational prioritization is coupled to experimental validation rather than used as a stand-alone generator. This makes fluorescent probes especially instructive for the broader field: AI performs best not when it replaces probe validation, but when it is embedded in a design framework where probe-relevant outputs are measurable and experimentally testable.

Covalent probe design highlights a different probe-specific dimension: the need to simultaneously consider intrinsic reactivity and residue or site selectivity. A major covalent probe subclass relevant here is ABPs, which, in their classical form, combine a reactive warhead with a linker or recognition element and a reporter tag or bioorthogonal handle for detection or enrichment (Porta and Steel, 2023). BIreactive combined ab initio activation energies with ML to estimate intrinsic warhead reactivity across mechanistically distinct classes, addressing the need to tune electrophiles rather than treat them as interchangeable (Palazzesi et al., 2020). At the protein-site level, DeepCoSI introduced structure-based deep graph learning to identify ligandable cysteines (Du et al., 2022), while subsequent work developed broader cysteine-ligandability models and extended prediction across experimentally determined and AlphaFold2-derived structures (Liu et al., 2024). Interpretable models such as CovCysPredictor further reinforce the distinction between generalized reactivity and selective covalent modifiability (Reimer et al., 2025). Collectively, these studies show that AI for covalent probes must reason over both chemical reactivity and local protein microenvironment, and that validation must remain proteome-aware if selective biological inference is the goal, particularly in light of recent proteome-wide comparisons showing how strongly electrophile selectivity can vary across probe classes (Zanon et al., 2025). This also illustrates a broader principle of probe-oriented AI: useful prediction is not just about finding reactive molecules, but about modeling the conditions under which chemical reactivity remains mechanistically interpretable.

2.4 Current limitations: datasets, validation, interpretability, and synthetic feasibility

The application examples above clarify where AI can already contribute to probe discovery; the next question is why these successes have not yet transferred more broadly across probe classes and use cases. The core limitations in AI-guided chemical probe design are not best understood simply as deficiencies in model performance, but as mismatches between what probe science requires and what current data resources, evaluation frameworks, validation workflows, and synthesis-aware design tools can support. This distinction matters because probe success depends not only on predicting molecular properties but also on generating compounds that can be synthesized, interpreted mechanistically, validated experimentally, and deployed under appropriate conditions of use. The principal bottlenecks, therefore, lie in four connected areas: dataset structure, validation and context dependence, interpretability, and chemical tractability.

2.4.1 Dataset limitations

Public bioactivity datasets and curated resources such as ChEMBL, BindingDB, Papyrus, and Drug Target Commons have substantially improved access to ligand–target measurements, but they are not structured around probe-validation criteria and often lack the standardized selectivity panels, assay comparability, and cell-context target-engagement metadata required for reliable chemical probe assessment (Béquignon et al., 2023; Tanoli et al., 2018; Liu et al., 2025; Hunter et al., 2025; Zdrazil et al., 2024). Probe-oriented resources, such as Probe Miner and the Chemical Probes Portal, make these missing dimensions more explicit; however, they remain constrained by what has been publicly measured, curated, and reported (Antolin et al., 2018; Antolin et al., 2023). The resulting data mismatch is not merely one of quantity. Rather, probe-relevant endpoints are especially difficult to encode as robust training labels because they are often sparse, heterogeneous, relational, or context-dependent. Broad selectivity matrices, matched control relationships, cell-context target-engagement data, and recommended conditions of use illustrate the problem particularly clearly. For example, a public dataset may contain potency measurements for a compound series, yet still lack the linked annotations that matter most for probe assessment: a standardized cross-target selectivity matrix, an explicitly matched inactive control, and a target-engagement readout in the relevant cellular context. In that setting, models can learn activity, but not the evidentiary structure needed to distinguish a plausible binder from a credible chemical probe. Progress will therefore require not only more probe-relevant data but also stronger consensus on how probe-critical endpoints are defined, annotated, and formatted as machine-usable labels.

2.4.2 Validation bottlenecks and context dependence

Probe standards require evidence regarding biochemical potency, selectivity, cellular target engagement, and appropriate controls. These validation steps are experimentally intensive, and factors such as cell line, exposure time, target expression, and orthogonal assay choice strongly influence interpretability. The observation that probes are often used outside recommended conditions demonstrates that even well-designed AI-prioritized molecules will not, by themselves, prevent misleading biological conclusions. Therefore, AI must support not only compound selection, but also validation strategy and context-appropriate use (Sanfelice et al., 2025; Sterling et al., 2023).

2.4.3 Interpretability and mechanisms

Chemical probe discovery is inherently mechanistic: the central question is not only whether a compound perturbs biology, but whether that perturbation can be confidently attributed to the intended target rather than to pathway ambiguity or off-target liabilities. Many high-performing predictive models, whether ligand-based, structure-based, or hybrid, remain difficult to interpret mechanistically, and even plausible model explanations can be confounded by bias, shortcut learning, or correlations that do not reflect causal chemistry–biology relationships. This concern is reinforced by examples from the goal-directed generative design literature, where optimization can exploit model artifacts and produce molecules that appear successful in silico while failing to support reliable mechanistic inference experimentally (Renz et al., 2019; Langevin et al., 2022). For chemical probes, this limitation is especially consequential because the central objective is not merely to generate active molecules, but to generate perturbations whose biological effects remain interpretable, with off-target biology minimized as far as possible.

2.4.4 Synthetic feasibility and chemical tractability

Although de novo design frameworks can propose molecules optimized against model-derived objectives, experimental progress ultimately depends on whether those structures can be synthesized, diversified, and evaluated within realistic medicinal chemistry constraints. Tools such as MolScore incorporate synthesizability-related scoring; however, these proxy measures do not guarantee short, robust, and project-compatible synthetic routes, nor do they ensure access to the analog series often required for probe optimization (Thomas et al., 2024). For chemical probe programs, tractability extends beyond making a single active compound and often includes the ability to prepare matched negative controls, isotopologues, tagged derivatives, or reactive variants needed for mechanism-of-action studies and probe deployment (Lee and Schapira, 2021; Hartung et al., 2023). Synthetic feasibility in probe discovery should, therefore, be understood not merely as route accessibility, but as derivatization- and control-enabled chemical tractability. In practice, this means either constraining the generation to synthetically accessible regions of chemical space or applying post hoc retrosynthetic analysis to deprioritize intractable structures (Guo and Schwaller, 2025).

2.5 AI-guided drug design versus AI-guided chemical probe design

Taken together, Table 1 makes clear that AI-guided chemical probe design should not be treated merely as an extension of AI-guided drug design. Here, “drug-centric” refers to the dominant optimization logic of conventional small-molecule drug discovery workflows, not to all therapeutic modalities, some of which can depart substantially from classical drug-likeness criteria. As outlined above, whereas drug-discovery workflows typically prioritize potency, exposure, safety, and developability, probe discovery places additional evidentiary weight on selectivity, demonstrable target engagement, mechanistic interpretability, appropriate concentration windows, and the availability of suitable control compounds. This distinction is particularly evident in the fluorescent and covalent probe examples discussed above. In fluorescent and other reporter-based probes, probe performance depends not only on target recognition but also on photophysical behavior and reporter performance under the intended assay conditions. In covalent probes, intrinsic reactivity and residue or site selectivity must be optimized to support selective biological inference rather than simply durable target occupancy. Probe success should, therefore, be understood not as a property of molecular structure alone, but as the product of the molecule, the supporting evidence, and the intended context of use. These distinctions align with established principles of probe quality and validation, including fit-for-purpose performance, selectivity, cellular engagement, negative controls, and modality-specific standards.

TABLE 1. Key differences between AI-guided drug design and AI-guided chemical probe design.

3 Conclusions and future directions

AI can make substantial contributions to small-molecule chemical probe discovery, but only if the field stops treating probe design as a straightforward extension of drug-candidate optimization. Chemical probes impose a distinct set of success criteria, centered on selectivity, target engagement, mechanistic interpretability, appropriate controls, and deployment under defined conditions of use, and these requirements demand a distinct computational and experimental framework. In our view, the most credible roadmap for AI-enabled chemical probe discovery rests on three linked priorities: probe-native data capture, probe-centric evaluation, and closed-loop experimentation.

First, the field needs datasets structured around probe quality criteria rather than potency alone. At minimum, these should include standardized biochemical potency, curated selectivity panels, cellular target-engagement measurements, explicit metadata on recommended conditions of use, and relationships between active probes and their controls. Existing expert-reviewed resources already encode part of this logic in human-readable form, suggesting a plausible path toward machine-readable schemas and supervised labels derived from expert judgment.

Second, probe-centric benchmarks should be designed explicitly to resist the known failure modes of goal-directed molecular optimization. Such benchmarks should explicitly evaluate out-of-distribution generalization and make uncertainty quantification and applicability-domain awareness first-class assessment criteria, rather than optional methodological add-ons. They should also penalize objectives that are experimentally infeasible or mechanistically incomplete. Existing infrastructures, such as GuacaMol, MOSES, and MolScore, provide useful starting points, but their objective functions would need to be reoriented around probe success criteria rather than inherited from drug-centric workflows.

Third, the most convincing path forward is closed-loop probe discovery, in which AI does not simply rank molecules but helps select the experiments that most directly reduce uncertainty about probe quality. DEL combined with ML already illustrates how large-scale experimental selection coupled with predictive modeling can accelerate the discovery of binders and clarify which workflows perform robustly in practice. For chemical probes, however, analogous loops must incorporate selectivity profiling, cellular engagement assays, and control-compound strategies early enough to influence design decisions rather than merely validate them retrospectively. Fluorescent probe discovery further shows that AI can succeed when large modality-specific datasets exist and computational prioritization is tightly integrated with assay workflows rather than being used as a stand-alone generator.

Finally, modality-specific probe design should be treated as a first-class design space rather than as a peripheral case. For fluorescent probes, continued scaling and standardization of photophysical datasets should improve inverse design and target-specific scaffold discovery. For covalent probes, combining warhead-reactivity models with structure-aware residue-ligandability predictors offers a principled route to balancing intrinsic reactivity with site selectivity, which is a defining probe requirement rather than a secondary optimization task. The current focus on cysteine reflects the fact that available datasets and predictive tools are most developed for cysteine-targeting covalent strategies.

At present, the strongest published evidence for probe-relevant AI comes from modalities in which probe-specific endpoints are unusually well defined and measurable at scale, particularly fluorescent and covalent probes. Extending AI-guided probe design across broader target classes and modalities will require datasets, benchmarks, and experimental workflows built around the real determinants of probe quality. The key challenge is therefore not simply to design better molecules, but to design better evidence-generating systems for reliable biological inference and target validation.

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Footnotes

Edited by: José L. Medina-Franco, National Autonomous University of Mexico, Mexico

Reviewed by: Ulises Martínez-Ortega, National Autonomous University of Mexico, Mexico