New approaches to drug discovery are urgently needed, especially for diseases with complex molecular mechanisms where current treatments show limited efficacy. We present a data-driven workflow for drug repositioning that integrates high-throughput and virtual screening, molecular docking, and mechanistic pharmacokinetic modeling with biological validation using lung fibrosis as a model disease. High-throughput screening assays and LINCS L1000, SwissTarget, and Unified Knowledge Space (UKS) databases identified ouabain and helenalin as candidates targeting lung fibrosis-related genes. Pharmacophore modeling, docking analysis, and physiologically based pharmacokinetic modeling confirmed drug-like properties comparable to current lung fibrosis treatments. In silico findings were validated using RNA-sequencing data from idiopathic pulmonary fibrosis patients and quantitative polymerase chain reaction data from human alveolar epithelial cells exposed to profibrotic transforming growth factor-β. In vitro, helenalin and ouabain induced distinct gene expression profiles. The anti-inflammatory signature observed for helenalin points to preliminary functional potential warranting further investigation. This workflow can be generalized across diverse therapeutic areas to accelerate cost-effective drug discovery and repositioning.

Toxicology faces the need to shift from generalized hazard evaluation toward precision approaches that account for the impact of the exposed biological systems. This need is particularly evident for per- and polyfluoroalkyl substances (PFAS), a highly persistent and diverse chemical class whose multiorgan apical toxicities are well documented, yet whose mechanistic understanding remains fragmented. To address this gap, a comprehensive toxicogenomic collection covering multiple PFAS and biological systems is curated, harmonized, and standardized. Through systematic integration of these data with the Adverse Outcome Pathway framework, biological context-aware key event networks that capture the progression from molecular initiating events to apical outcomes are reconstructed. Additionally, new transcriptomic profiles from macrophages exposed to seven PFAS are generated to address the critical but under-investigated immune-related context. Analysis reveals that PFAS toxicity arises from shared early molecular perturbations that diverge across biological systems to produce organ-specific outcomes where immune-related processes consistently emerge as central contributors across multiple contexts. In the liver emerges a conserved mechanistic core underlying PFAS-induced steatosis activated through system-specific pathways shaped by PFAS physicochemical properties and biological context. Overall, this work provides a framework for advancing precision toxicology, enabling rapid, mechanistically grounded, and context-aware PFAS hazard characterization, and supporting the prioritization of uncharacterized PFAS based on shared and context-dependent mechanistic patterns.

Abstract New Approach Methodologies (NAMs) are increasingly promoted for animal-free nanosafety assessment, yet their regulatory use remains constrained by fragmented regulatory criteria and nanomaterial-specific testing complexities. This review comprehensively analyzes the convergence of advanced cell biology, nanotechnology, and information technology in reshaping safety evaluations, with a focus on regulatory translation across global frameworks. We first summarize enabling technologies, including advanced in vitro and ex vivo systems, organoids, microphysiological systems, high-dimensional single-cell multi-omics and label-free hyperspectral imaging, AutoML, generative AI, physiologically based kinetic modeling, and in vitro-to-in vivo extrapolation. We then analyze the regulatory landscape, emphasizing OECD harmonization efforts, the Mutual Acceptance of Data system, and regional developments across Europe, Africa, the Americas, and Asia–Pacific. A central theme is that regulatory acceptance of NAMs for nanomaterials requires not only biological relevance, reproducibility, and context-of-use definition, but also robust physicochemical characterization, exposure control, dosimetry, and data interoperability. Industrial implementation in pharmaceuticals, cosmetics, agrochemicals, antimicrobials, and environmental protection is discussed as a measure of regulatory readiness and scalability. Finally, we outline future directions involving Smart NAMs, digital twins, and Safe and Sustainable by Design (SSbD) frameworks. By integrating technological, regulatory, and implementation perspectives, this review identifies pathways toward harmonized, mechanistic, and human- or target species-relevant next-generation risk assessment (NGRA) for nanotechnology. Graphical abstract

Background Disentangling physiopathological mechanisms of biological systems through high-level integration of omics data has become a standard procedure in life sciences. However, platform heterogeneity, batch effects, and the lack of unified methods for single-and multi-omics analyses represent relevant drawbacks that hinder the extrapolation of a meaningful biological interpretation. While statistical meta-analysis is widely used to integrate several omics datasets of the same type, it does not allow the integration of multi-modal data deriving from multi-omics experiments. Network science is at the forefront of systems biology, where the inference of molecular interactomes allowed the investigation of perturbed biological systems, by shedding light on the disrupted relationships that keep the homeostasis of complex systems. Results Here, we present MUUMI, an R package that unifies statistical meta-analysis and network-based omics data integration within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytical methods, inference and analysis of molecular interactomes and the integration of multiple omics layers through similarity network fusion. We demonstrate the functionalities of MUUMI by presenting two case studies in which we analysed (1) 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and (2) multi-omics data of THP-1 macrophages exposed to different polarising stimuli. In both examples, MUUMI revealed biologically coherent signatures, underscoring its value in elucidating complex biological processes. Conclusions MUUMI leverages omics data meta-analysis, integration and interpretation that implements both traditional and network-based approaches to unleash the power of multi-study datasets. Statistical and network-based approaches are integrated in a unique framework, allowing the user to derive robust and biologically meaningful results from different studies and datasets. MUUMI is an open-source package and is freely available at https://github.com/fhaive/muumi.

Rapid innovation in chemicals and materials calls for innovative integrated approaches that can assess their impacts across different areas. The Safe and Sustainable-by-Design (SSbD) framework, developed by the European Commission's Joint Research Centre (JRC), offers a comprehensive approach with which to evaluate the safety and sustainability of chemicals and materials across their lifecycle. While SSbD uses various modeling approaches to assess impacts on human health, the environment, and socioeconomic factors, these are often applied independently, hindering a holistic understanding of the complex interactions between these factors and thus the simultaneous optimization of function, cost, safety and sustainability. This review describes existing predictive models and available strategies for their integration to facilitate more comprehensive and holistic chemical and material impact assessments. Specifically, we examine three model integration strategies: consensus integration that combines model predictions for the same impact categories, weighted aggregation that combines different scores in a unified one, and pipeline integration that links models sequentially to create a more unified assessment. Furthermore, we address key concepts related to the uncertainty of model predictions and the applicability domain of models, highlighting how these evolve in integrated frameworks. Insights into the applications of these integration strategies and challenges will allow a more accurate, coherent, and sustainable approach to chemical and material safety and sustainability assessments.

Despite the advent of mechanistic toxicology using omics data to link molecular perturbations with systemic outcomes, regulatory toxicology still lacks the application of mechanism-anchored metrics from such data. This is partially because traditional gene-centric analysis often falls short of linking molecular changes to adverse outcomes. To address this gap, BMDx2, an open-source tool that transforms multi-dose toxicogenomics datasets into quantitative, mechanistic evidence for human chemical safety assessment is developed. BMDx2 couples benchmark-dose modeling with Adverse Outcome Pathway (AOP) enrichment to derive transcriptomic-based points of departure, enabling potency ranking, chemical prioritization, and mechanistically anchored explanations of the effect of chemical exposures. BMDx2 can process a broad range of data, including DNA microarray and RNA sequencing studies. Here, case studies are used to illustrate the versatility of BMDx2 in characterizing the mechanism of action of chemicals. An initial case study on carbon nanotubes exposure applies integrative analysis of transcriptomics and genome-wide DNA methylation data, uncovering cellular reprogramming processes underlying fibrosis. A second case study on bleomycin exposure demonstrate how transcriptomic data alone can be mapped to fibrosis-related AOPs in a standardized, regulatory appropriate manner. Together, these examples show how BMDx2 supports the regulatory application of toxicogenomics and accelerates mechanism-based chemical safety evaluation.

The development of new approach methodologies (NAMs) to replace current in vivo testing for the safety assessment of engineered nanomaterials (ENMs) is hindered by the scarcity of validated experimental data for many ENMs. We introduce a framework to address this challenge by harnessing the collective expertise of professionals from multiple complementary and related fields ("wisdom of crowds" or WoC). By integrating expert insights, we aim to fill data gaps and generate consensus concern scores for diverse ENMs, thereby enhancing the predictive power of nanosafety computational models. Our investigation reveals an alignment between expert opinion and experimental data, providing robust estimations of concern levels. Building upon these findings, we employ predictive machine learning models trained on the newly defined concern scores, ENM descriptors, and gene expression profiles, to quantify potential harm across various toxicity end points. These models further reveal key genes potentially involved in underlying toxicity mechanisms. Notably, genes associated with metal ion homeostasis, inflammation, and oxidative stress emerge as predictors of ENM toxicity across diverse end points. This study showcases the value of integrating expert knowledge and computational modeling to support more efficient, mechanism-informed, and scalable safety assessment of nanomaterials in the rapidly evolving landscape of nanotechnology.

INTRODUCTION:Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge. AREAS COVERED:This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area. EXPERT OPINION:Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.

This study provides manually curated and homogenised transcriptomics data of interstitial lung disease (ILD) patients retrieved from the NCBI Gene Expression Omnibus and European Nucleotide Archive repositories. The compendium includes 30 transcriptomics datasets generated with DNA microarrays and RNA sequencing (RNA-seq) technologies for a total of 1371 samples. All the datasets underwent metadata curation and harmonisation, data quality check, and preprocessing with standardised procedures. Furthermore, a robust data model was developed to standardise phenotypic data, thereby enhancing comparability across heterogeneous datasets. Gene expression data and lists of differentially expressed genes computed between ILD and healthy samples are provided. Among the ILDs included in this study, idiopathic pulmonary fibrosis (IPF) is the most represented worldwide. Co-expression networks of IPF and healthy samples were inferred, which are also included in this study. This study enhances the Findability, Accessibility, Interoperability, and Reusability (FAIR) of publicly available transcriptomic datasets related to ILDs. The resulting resource provides a integrated platform for the implementation and validation of systems biology and pharmacology approaches, facilitating the development of novel diagnostic and therapeutic strategies for ILDs.

Pulmonary fibrosis, a progressive and debilitating disease, presents a significant global health challenge. Even though often idiopathic, drug-induced fibrosis is increasing its incidence. Traditional chemical safety assessments, relying on apical endpoints from in-vivo models, are limited in capturing the early molecular events initiating fibrosis, consequently limiting the potential for early diagnosis and mechanism-driven treatment. This study employed a toxicogenomic approach on in-vitro MRC-5 fibroblasts, a crucial cell type involved in fibrosis, to dissect the initiating profibrotic mechanisms of Bleomycin (1, 1.5, 2 mu g/mL), a profibrotic triggering stimulus, comparing it with TGF(3-1(5, 10, 15 ng/mL), a known sustaining mediator of fibrosis over 24, 48, and 72 h. Our analysis reveals that while both agents alter matrix-related processes, their initiation mechanisms diverge. Specifically, TGF(3-1 directly induces myofibroblast transition, whereas Bleomycin potentially induces an indirect transition through the establishment of a senescence-associated secretory phenotype (SASP). By capturing the early SASP signature, we identified a critical driver of Bleomycin-induced fibroblast fibrosis, relevant to druginduced fibrosis where antineoplastic agents are a major concern. This study underscores the critical importance of integrating mechanistic understanding into chemical safety assessment, thereby facilitating the development and implementation of safer, more sustainable chemical development.

Traditional in vivo methodologies have long formed the foundation of chemical and material safety assessment, yet they are increasingly inadequate to meet modern regulatory, ethical, and sustainability demands. These conventional approaches are resource-intensive, ethically questionable, and often fail to accurately predict human or environmental toxicity, particularly for emerging pollutants such as PFAS, (nano-) pesticides, and 2D materials. In response, the EU has launched initiatives like the Chemical Strategy for Sustainability and the Zero Pollution Action Plan under the European Green Deal to promote innovation in safer, and more sustainable chemicals. Central to this transformation is the Safe and Sustainable by Design (SSbD) framework, developed by the European Commission’s Joint Research Center, which provides structured methodologies and metrics to integrate safety and sustainability into material innovation from the earliest stages of design.Building on this vision, the CHIASMA project aims to advance Next generation Safety Assessment (NGSA) by developing innovative New Approach Methodologies (NAMs) that combine experimental, computational, and Life Cycle Assessment (LCA) tools. Focusing on key biological systems and exposure routes, CHIASMA integrates Artificial Intelligence (AI), Machine Learning (ML), and Knowledge Graph (KG) technologies to enhance data interoperability and predictive accuracy. By embedding FAIR data principles and aligning with Good Laboratory Practice (GLP) standards, CHIASMA promotes transparency and regulatory acceptance. Fully aligned with SSbD principles, CHIASMA establishes a digital, interoperable infrastructure for predictive safety evaluation, that leverage on state-of-art experimental New Approach Methodologies (NAMs) bridging critical data gaps and supporting the transition towards sustainable, science-driven, and ethically responsible chemical and material innovation in Europe and beyond.

Grouping is a fundamental step to generalize engineered nanomaterials (ENM) hazard. However, most strategies lack comprehensiveness in ENM and experimental settings. Toxicogenomics allows the characterization of the direct molecular mechanisms of action (MOA) associated to ENM hazard. In this study, we implemented an adverse outcome pathway (AOP)-based framework for ENM grouping. We hypothesized that AOP-direct MOA along with ENM potency, i.e., the dose required to activate a molecular response, could be used to robustly group distinct ENM exposures by considering a mechanistic and multiscale level of response. Our results highlighted a critical role of the exposure duration and ENM potency respectively on the specificity and progression of the response. Moreover, we investigated the complexity and time scale of the biological events triggered by ENM. In particular, genotoxicity-related AOPs were found triggered at longer exposures. Higher ENM potency was linked to shorter exposure and basic and starting events. While lower potency was linked to prolonged exposures and advanced stages of biological processes. Our results also highlighted shared molecular responses in groups of ENM both at shorter (5 clusters) and longer (3 clusters) exposure periods. Based on computed features for cluster predictions, grouping could have likely resulted from the influence of chemical composition, size-dependent properties of ENM, and biological descriptors. Finally, features were interdependent and differed in quantity and connectivity, indicating differences in the response dynamics. Notably, our study does not include every ENM currently available. Hereby, our findings pave the way for the construction of quantitative AOPs and hold significant implications for ENM hazard assessment and regulatory decision-making.

SUMMARY:OpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AVAILABILITY AND IMPLEMENTATION:OpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961292.

Pulmonary fibrosis (PF) is a life-threatening condition characterised by excessive extracellular matrix deposition and tissue scarring. While much of PF research has focused on alveolar epithelial cells and fibroblasts, endothelial cells have emerged as active contributors to the disease initiation, especially in the context of systemic exposure to pro-fibrotic substances. Here, we investigate early transcriptomic and secretory responses of human umbilical vein endothelial cells (HUVEC) to subtoxic doses of bleomycin, a known pro-fibrotic agent, and TGF-beta, a key cytokine in fibrosis. Bleomycin exposure induced a rapid and extensive shift in the endothelial transcriptional programme, including signatures of endothelial to mesenchymal transition, cellular senescence, and immune cell recruitment. These findings suggest endothelial cells as early initiators of pro-fibrotic signals, independent of contributions from other cell types. In contrast, TGF-beta effects were limited and transient, indicating its pro-fibrotic action may require another initial stimulus and interplay with other cells like fibroblasts. This study highlights the sensitivity of endothelial cells to pro-fibrotic exposure and provides a blueprint of early pro-fibrotic mechanisms that may operate on organs such as the lungs systemically via the endothelium, emphasising its pivotal role in PF pathogenesis.

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework. The EU INSIGHT project addresses this challenge by developing a novel computational framework for integrated impact assessment, based on the Impact Outcome Pathway (IOP) approach. Extending the Adverse Outcome Pathway (AOP) concept, IOPs establish mechanistic links between chemical and material properties and their environmental, health, and socio-economic consequences. The project integrates multi-source datasets (including omics, life cycle inventories, and exposure models) into a structured knowledge graph (KG), ensuring FAIR (Findable, Accessible, Interoperable, Reusable) data principles are met. INSIGHT is being developed and validated through four case studies targeting per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings. These studies demonstrate how multi-model simulations, decision-support tools, and artificial intelligence-driven knowledge extraction can enhance the predictability and interpretability of chemical and material impacts. Additionally, INSIGHT incorporates interactive, web-based decision maps to provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments. By bridging mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis, INSIGHT advances a scalable, transparent, and data-driven approach to SSbD. This project aligns with the European Green Deal and global sustainability goals, promoting safer, more sustainable innovation in chemicals and materials through an integrated, mechanistic, and computationally advanced framework.

The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.

Pulmonary fibrosis, a progressive and debilitating disease, presents a significant global health challenge. Even though often idiopathic, drug-induced fibrosis is increasing its incidence. Traditional chemical safety assessments, relying on apical endpoints from in-vivo models, are limited in capturing the early molecular events initiating fibrosis, consequently limiting the potential for early diagnosis and mechanism-driven treatment. This study employed a toxicogenomic approach on in-vitro MRC-5 fibroblasts, a crucial cell type involved in fibrosis, to dissect the initiating profibrotic mechanisms of Bleomycin (1, 1.5, 2 μg/mL), a profibrotic triggering stimulus, comparing it with TGFβ-1(5, 10, 15 ng/mL), a known sustaining mediator of fibrosis over 24, 48, and 72 h. Our analysis reveals that while both agents alter matrix-related processes, their initiation mechanisms diverge. Specifically, TGFβ-1 directly induces myofibroblast transition, whereas Bleomycin potentially induces an indirect transition through the establishment of a senescence-associated secretory phenotype (SASP). By capturing the early SASP signature, we identified a critical driver of Bleomycin-induced fibroblast fibrosis, relevant to drug-induced fibrosis where antineoplastic agents are a major concern. This study underscores the critical importance of integrating mechanistic understanding into chemical safety assessment, thereby facilitating the development and implementation of safer, more sustainable chemical development.

In this innovation report, we present the vision of the PINK project to foster Safe-and-Sustainable-by-Design (SSbD) advanced materials and chemicals (AdMas&Chems) development by integrating state-of-the-art computational modelling, simulation tools and data resources. PINK proposes a novel approach for the use of the SSbD Framework, whose innovative approach is based on the application of a multi-objective optimisation procedure for the criteria of functionality, safety, sustainability and cost efficiency. At the core is the PINK open innovation platform, a distributed system that integrates all relevant modelling resources enriched with advanced data visualisation and an AI-driven decision support system. Data and modelling tools from the, in large parts, independently developed areas of functional design, safety assessment, life cycle assessment & costing are brought together based on a newly created Interoperability Framework. The PINK In Silico Hub, as the user Interface to the platform, finally guides the user through the complete AdMas&Chems development process from idea creation to market introduction. Guided by two Developmental Case Studies, the process of building of the PINK Platform is iterative, ensuring industry readiness to implement and apply it. Additionally, the Industrial Demonstrator programme will be introduced as part of the final project phase, which allows industry partners and especially small and medium enterprises (SMEs) to become part of the PINK consortium. Feedback from the Demonstrators as well as other stakeholder-engagement activities and collaborations will shape the platform's final look and feel and, even more important, activities to assure long-term technical sustainability.

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

The identification of safe and effective compounds is at the core of the compound development process. The richness of publicly available datasets opened unprecedent horizons for the development of data-driven methodologies. However, traditional methods often neglect the interactions between the compounds and the exposed biological systems, resulting in uncomprehensive safety and efficacy assessment. Toxicogenomics aims at explaining the underlying biological mechanisms associated with compound exposures and at linking them with their efficacy and safety assessment. Moreover, by following the 3R principles (replacement, reduction, refinement of animal experiments), toxicogenomic-based approaches can facilitate the reduction of in vivo assays with in vitro testing. Methodologies that combine cheminformatics and bioinformatics data and techniques have the potential to advance the development of new effective and safe compounds. In this chapter, we identified relevant datasets for the development of integrative models, we review existing computational models, and we discuss their methodologies to promote their growth in the compound development field.