{"id":56536,"date":"2025-12-09T07:59:53","date_gmt":"2025-12-09T07:59:53","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=56536"},"modified":"2026-01-05T08:28:42","modified_gmt":"2026-01-05T08:28:42","slug":"top-10-drug-discovery-platforms-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-drug-discovery-platforms-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Drug Discovery Platforms: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/strong><\/h2>\n\n\n\n

Drug discovery is one of the most complex, time-consuming, and expensive processes in modern science. From identifying a promising biological target to validating a safe and effective drug candidate, research teams must navigate massive datasets, intricate molecular interactions, regulatory constraints, and long development timelines. Drug Discovery Platforms<\/strong> are specialized software solutions designed to streamline, accelerate, and de-risk this process by combining computational modeling, data analytics, artificial intelligence (AI), and experimental insights into a unified environment.<\/p>\n\n\n\n

These platforms play a critical role in pharmaceutical R&D<\/strong>, biotechnology innovation<\/strong>, academic research<\/strong>, and contract research organizations (CROs)<\/strong>. They help scientists identify viable targets, design molecules, predict toxicity, optimize lead compounds, and manage discovery workflows more efficiently than traditional, siloed methods.<\/p>\n\n\n\n

When choosing a drug discovery platform, users should evaluate factors such as scientific depth<\/strong>, AI and modeling capabilities<\/strong>, data integration<\/strong>, ease of use<\/strong>, scalability<\/strong>, security and compliance<\/strong>, and total cost of ownership<\/strong>. The right platform can significantly reduce discovery timelines, lower costs, and improve the probability of clinical success\u2014while the wrong choice can slow teams down and create integration headaches.<\/p>\n\n\n\n

Best for:<\/strong>
Drug Discovery Platforms are best suited for pharmaceutical companies, biotech startups, academic labs, CROs, computational chemists, bioinformaticians, medicinal chemists, and translational research teams working on small molecules, biologics, or novel therapeutic modalities.<\/p>\n\n\n\n

Not ideal for:<\/strong>
They may be excessive for organizations with minimal R&D activity, purely clinical operations without discovery needs, or very small labs that only require basic data analysis tools rather than full-scale discovery platforms.<\/p>\n\n\n\n

\n\n\n\n

Top 10 Drug Discovery Platforms Tools<\/strong><\/h2>\n\n\n\n
\n\n\n\n

1 \u2014 Schr\u00f6dinger<\/strong><\/h3>\n\n\n\n

Short description:<\/strong>
Schr\u00f6dinger is a leading computational drug discovery platform focused on physics-based molecular modeling and simulation. It is widely used by medicinal chemists and pharmaceutical R&D teams for structure-based drug design.<\/p>\n\n\n\n

Key features:<\/strong><\/p>\n\n\n\n

    \n
  • Advanced molecular modeling and simulation engines<\/li>\n\n\n\n
  • Structure-based drug design and ligand docking<\/li>\n\n\n\n
  • Free energy perturbation (FEP+) for potency prediction<\/li>\n\n\n\n
  • Integrated cheminformatics and visualization tools<\/li>\n\n\n\n
  • Support for small molecules and biologics<\/li>\n\n\n\n
  • Cloud and on-premise deployment options<\/li>\n<\/ul>\n\n\n\n

    Pros:<\/strong><\/p>\n\n\n\n

      \n
    • Highly accurate physics-based models<\/li>\n\n\n\n
    • Trusted by top pharmaceutical companies<\/li>\n<\/ul>\n\n\n\n

      Cons:<\/strong><\/p>\n\n\n\n

        \n
      • Steep learning curve for non-computational users<\/li>\n\n\n\n
      • Premium pricing limits accessibility for small teams<\/li>\n<\/ul>\n\n\n\n

        Security & compliance:<\/strong>
        Enterprise-grade security, encryption, access controls; compliance varies by deployment.<\/p>\n\n\n\n

        Support & community:<\/strong>
        Strong documentation, professional training, enterprise support, and an active scientific user community.<\/p>\n\n\n\n

        \n\n\n\n

        2 \u2014 Benchling<\/strong><\/h3>\n\n\n\n

        Short description:<\/strong>
        Benchling is a cloud-native R&D platform that supports drug discovery through integrated data management, collaboration, and workflow automation, particularly in biotech environments.<\/p>\n\n\n\n

        Key features:<\/strong><\/p>\n\n\n\n

          \n
        • Unified platform for experimental and discovery data<\/li>\n\n\n\n
        • Molecular biology and chemistry data modeling<\/li>\n\n\n\n
        • Workflow automation and version control<\/li>\n\n\n\n
        • Real-time collaboration across teams<\/li>\n\n\n\n
        • API and integration ecosystem<\/li>\n\n\n\n
        • Cloud-first architecture<\/li>\n<\/ul>\n\n\n\n

          Pros:<\/strong><\/p>\n\n\n\n

            \n
          • Excellent usability and collaboration features<\/li>\n\n\n\n
          • Scales well from startups to enterprises<\/li>\n<\/ul>\n\n\n\n

            Cons:<\/strong><\/p>\n\n\n\n

              \n
            • Limited deep computational modeling<\/li>\n\n\n\n
            • Advanced analytics may require integrations<\/li>\n<\/ul>\n\n\n\n

              Security & compliance:<\/strong>
              SOC 2, GDPR alignment, role-based access, audit logs.<\/p>\n\n\n\n

              Support & community:<\/strong>
              Strong onboarding, responsive support, and a large biotech user base.<\/p>\n\n\n\n

              \n\n\n\n

              3 \u2014 Cresset<\/strong><\/h3>\n\n\n\n

              Short description:<\/strong>
              Cresset specializes in ligand-based drug discovery using electrostatic field modeling to identify and optimize novel compounds.<\/p>\n\n\n\n

              Key features:<\/strong><\/p>\n\n\n\n

                \n
              • Field-based molecular modeling<\/li>\n\n\n\n
              • Ligand similarity and scaffold hopping<\/li>\n\n\n\n
              • Lead optimization workflows<\/li>\n\n\n\n
              • Intuitive visualization tools<\/li>\n\n\n\n
              • Integration with third-party chemistry software<\/li>\n<\/ul>\n\n\n\n

                Pros:<\/strong><\/p>\n\n\n\n

                  \n
                • Unique field-based approach complements structure-based methods<\/li>\n\n\n\n
                • Relatively easy to adopt<\/li>\n<\/ul>\n\n\n\n

                  Cons:<\/strong><\/p>\n\n\n\n

                    \n
                  • Narrower scope than end-to-end platforms<\/li>\n\n\n\n
                  • Less suitable for biologics<\/li>\n<\/ul>\n\n\n\n

                    Security & compliance:<\/strong>
                    Varies \/ N\/A depending on deployment.<\/p>\n\n\n\n

                    Support & community:<\/strong>
                    Good documentation, training resources, and direct scientific support.<\/p>\n\n\n\n

                    \n\n\n\n

                    4 \u2014 BIOVIA<\/strong><\/h3>\n\n\n\n

                    Short description:<\/strong>
                    BIOVIA offers an extensive suite of tools covering data management, modeling, simulation, and informatics for end-to-end drug discovery and development.<\/p>\n\n\n\n

                    Key features:<\/strong><\/p>\n\n\n\n

                      \n
                    • Comprehensive discovery and modeling portfolio<\/li>\n\n\n\n
                    • Data management and ELN integration<\/li>\n\n\n\n
                    • Materials science and biological modeling<\/li>\n\n\n\n
                    • Scalable enterprise deployment<\/li>\n\n\n\n
                    • Strong interoperability across R&D<\/li>\n<\/ul>\n\n\n\n

                      Pros:<\/strong><\/p>\n\n\n\n

                        \n
                      • Very broad and mature platform<\/li>\n\n\n\n
                      • Enterprise-ready with deep capabilities<\/li>\n<\/ul>\n\n\n\n

                        Cons:<\/strong><\/p>\n\n\n\n

                          \n
                        • Complex to implement<\/li>\n\n\n\n
                        • High total cost of ownership<\/li>\n<\/ul>\n\n\n\n

                          Security & compliance:<\/strong>
                          Enterprise security, audit trails, and regulatory support.<\/p>\n\n\n\n

                          Support & community:<\/strong>
                          Professional services, global enterprise support, and extensive documentation.<\/p>\n\n\n\n

                          \n\n\n\n

                          5 \u2014 Atomwise<\/strong><\/h3>\n\n\n\n

                          Short description:<\/strong>
                          Atomwise leverages deep learning and AI to predict molecular binding and accelerate early-stage drug discovery.<\/p>\n\n\n\n

                          Key features:<\/strong><\/p>\n\n\n\n

                            \n
                          • AI-driven structure-based screening<\/li>\n\n\n\n
                          • Deep learning models for target binding<\/li>\n\n\n\n
                          • Virtual compound screening<\/li>\n\n\n\n
                          • Focus on early discovery acceleration<\/li>\n\n\n\n
                          • Cloud-based infrastructure<\/li>\n<\/ul>\n\n\n\n

                            Pros:<\/strong><\/p>\n\n\n\n

                              \n
                            • Significant speed gains in hit identification<\/li>\n\n\n\n
                            • Strong AI specialization<\/li>\n<\/ul>\n\n\n\n

                              Cons:<\/strong><\/p>\n\n\n\n

                                \n
                              • Limited transparency into models<\/li>\n\n\n\n
                              • Less control for hands-on chemists<\/li>\n<\/ul>\n\n\n\n

                                Security & compliance:<\/strong>
                                Data security and access controls; compliance varies.<\/p>\n\n\n\n

                                Support & community:<\/strong>
                                Partner-focused support model with limited open community.<\/p>\n\n\n\n

                                \n\n\n\n

                                6 \u2014 Exscientia<\/strong><\/h3>\n\n\n\n

                                Short description:<\/strong>
                                Exscientia combines AI, automation, and experimental science to design optimized drug candidates faster than traditional methods.<\/p>\n\n\n\n

                                Key features:<\/strong><\/p>\n\n\n\n

                                  \n
                                • AI-driven molecule design<\/li>\n\n\n\n
                                • Integrated experimental feedback loops<\/li>\n\n\n\n
                                • Predictive ADMET modeling<\/li>\n\n\n\n
                                • Focus on productivity and speed<\/li>\n\n\n\n
                                • Strong pharma partnerships<\/li>\n<\/ul>\n\n\n\n

                                  Pros:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • Proven success in rapid candidate progression<\/li>\n\n\n\n
                                  • Deep integration of AI and biology<\/li>\n<\/ul>\n\n\n\n

                                    Cons:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Less accessible as a standalone platform<\/li>\n\n\n\n
                                    • Primarily partnership-driven model<\/li>\n<\/ul>\n\n\n\n

                                      Security & compliance:<\/strong>
                                      Enterprise-grade security; compliance handled through partnerships.<\/p>\n\n\n\n

                                      Support & community:<\/strong>
                                      Dedicated partner support, limited general user community.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      7\u2014 Deep Genomics<\/strong><\/h3>\n\n\n\n

                                      Short description:<\/strong>
                                      Deep Genomics focuses on RNA-based therapeutics, using AI to interpret genetic variation and identify novel drug targets.<\/p>\n\n\n\n

                                      Key features:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • AI-driven genomics analysis<\/li>\n\n\n\n
                                      • RNA biology modeling<\/li>\n\n\n\n
                                      • Variant effect prediction<\/li>\n\n\n\n
                                      • Target discovery for rare and complex diseases<\/li>\n\n\n\n
                                      • Cloud-based analytics<\/li>\n<\/ul>\n\n\n\n

                                        Pros:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Strong specialization in RNA therapeutics<\/li>\n\n\n\n
                                        • Cutting-edge AI research<\/li>\n<\/ul>\n\n\n\n

                                          Cons:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • Narrow therapeutic focus<\/li>\n\n\n\n
                                          • Not a general-purpose discovery platform<\/li>\n<\/ul>\n\n\n\n

                                            Security & compliance:<\/strong>
                                            Data protection and controlled access; compliance varies.<\/p>\n\n\n\n

                                            Support & community:<\/strong>
                                            Research-oriented support with limited public community.<\/p>\n\n\n\n

                                            \n\n\n\n

                                            8 \u2014 OpenEye<\/strong><\/h3>\n\n\n\n

                                            Short description:<\/strong>
                                            OpenEye provides cheminformatics and molecular modeling tools that support ligand-based and structure-based drug discovery.<\/p>\n\n\n\n

                                            Key features:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • Cheminformatics toolkits<\/li>\n\n\n\n
                                            • Molecular docking and scoring<\/li>\n\n\n\n
                                            • Shape-based screening<\/li>\n\n\n\n
                                            • Cloud-ready APIs<\/li>\n\n\n\n
                                            • Developer-friendly architecture<\/li>\n<\/ul>\n\n\n\n

                                              Pros:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Highly flexible for custom workflows<\/li>\n\n\n\n
                                              • Strong performance and speed<\/li>\n<\/ul>\n\n\n\n

                                                Cons:<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Requires technical expertise<\/li>\n\n\n\n
                                                • Limited out-of-the-box workflows<\/li>\n<\/ul>\n\n\n\n

                                                  Security & compliance:<\/strong>
                                                  Varies \/ N\/A depending on deployment.<\/p>\n\n\n\n

                                                  Support & community:<\/strong>
                                                  Solid documentation and developer-focused support.<\/p>\n\n\n\n

                                                  \n\n\n\n

                                                  9 \u2014 Insilico Medicine<\/strong><\/h3>\n\n\n\n

                                                  Short description:<\/strong>
                                                  Insilico Medicine applies AI across target discovery, molecule generation, and aging-related research.<\/p>\n\n\n\n

                                                  Key features:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • Generative AI for molecule design<\/li>\n\n\n\n
                                                  • Target identification algorithms<\/li>\n\n\n\n
                                                  • End-to-end AI discovery pipelines<\/li>\n\n\n\n
                                                  • Focus on novel targets and diseases<\/li>\n\n\n\n
                                                  • Cloud-based platform<\/li>\n<\/ul>\n\n\n\n

                                                    Pros:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Strong innovation in generative models<\/li>\n\n\n\n
                                                    • Broad AI-driven discovery scope<\/li>\n<\/ul>\n\n\n\n

                                                      Cons:<\/strong><\/p>\n\n\n\n

                                                        \n
                                                      • Limited transparency into algorithms<\/li>\n\n\n\n
                                                      • Best suited for AI-centric teams<\/li>\n<\/ul>\n\n\n\n

                                                        Security & compliance:<\/strong>
                                                        Enterprise data controls; compliance varies by engagement.<\/p>\n\n\n\n

                                                        Support & community:<\/strong>
                                                        Partner-centric support with growing industry recognition.<\/p>\n\n\n\n

                                                        \n\n\n\n

                                                        10 \u2014 ChemAxon<\/strong><\/h3>\n\n\n\n

                                                        Short description:<\/strong>
                                                        ChemAxon provides cheminformatics platforms and tools that support compound design, analysis, and data management.<\/p>\n\n\n\n

                                                        Key features:<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • Chemical structure editing and analysis<\/li>\n\n\n\n
                                                        • Property prediction and calculation<\/li>\n\n\n\n
                                                        • Data standardization tools<\/li>\n\n\n\n
                                                        • Integration-friendly APIs<\/li>\n\n\n\n
                                                        • Scalable enterprise deployment<\/li>\n<\/ul>\n\n\n\n

                                                          Pros:<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • Widely adopted cheminformatics foundation<\/li>\n\n\n\n
                                                          • Flexible integration options<\/li>\n<\/ul>\n\n\n\n

                                                            Cons:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • Not a full end-to-end discovery suite<\/li>\n\n\n\n
                                                            • Requires complementary tools<\/li>\n<\/ul>\n\n\n\n

                                                              Security & compliance:<\/strong>
                                                              Enterprise security options; compliance varies.<\/p>\n\n\n\n

                                                              Support & community:<\/strong>
                                                              Strong documentation, training, and enterprise support.<\/p>\n\n\n\n

                                                              \n\n\n\n

                                                              Comparison Table<\/strong><\/h2>\n\n\n\n
                                                              Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                              Schr\u00f6dinger<\/td>Structure-based drug design<\/td>Windows, Linux, Cloud<\/td>Physics-based accuracy<\/td>N\/A<\/td><\/tr>
                                                              Benchling<\/td>Collaborative biotech R&D<\/td>Cloud<\/td>Unified R&D workspace<\/td>N\/A<\/td><\/tr>
                                                              Cresset<\/td>Ligand-based discovery<\/td>Windows, Linux<\/td>Field-based modeling<\/td>N\/A<\/td><\/tr>
                                                              BIOVIA<\/td>Enterprise drug discovery<\/td>Cloud, On-prem<\/td>End-to-end suite<\/td>N\/A<\/td><\/tr>
                                                              Atomwise<\/td>AI hit discovery<\/td>Cloud<\/td>Deep learning screening<\/td>N\/A<\/td><\/tr>
                                                              Exscientia<\/td>AI-driven design<\/td>Cloud<\/td>AI + automation<\/td>N\/A<\/td><\/tr>
                                                              Deep Genomics<\/td>RNA therapeutics<\/td>Cloud<\/td>Genomics AI<\/td>N\/A<\/td><\/tr>
                                                              OpenEye<\/td>Custom modeling workflows<\/td>Cloud, On-prem<\/td>Developer APIs<\/td>N\/A<\/td><\/tr>
                                                              Insilico Medicine<\/td>Generative AI discovery<\/td>Cloud<\/td>AI molecule generation<\/td>N\/A<\/td><\/tr>
                                                              ChemAxon<\/td>Cheminformatics foundation<\/td>Cloud, On-prem<\/td>Chemical intelligence<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Evaluation & Scoring of Drug Discovery Platforms<\/strong><\/h2>\n\n\n\n
                                                              Criteria<\/th>Weight<\/th>Description<\/th><\/tr><\/thead>
                                                              Core features<\/td>25%<\/td>Breadth and depth of discovery capabilities<\/td><\/tr>
                                                              Ease of use<\/td>15%<\/td>Learning curve and usability<\/td><\/tr>
                                                              Integrations & ecosystem<\/td>15%<\/td>Compatibility with R&D tools<\/td><\/tr>
                                                              Security & compliance<\/td>10%<\/td>Data protection and auditability<\/td><\/tr>
                                                              Performance & reliability<\/td>10%<\/td>Scalability and computational efficiency<\/td><\/tr>
                                                              Support & community<\/td>10%<\/td>Documentation, training, support<\/td><\/tr>
                                                              Price \/ value<\/td>15%<\/td>ROI relative to cost<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Which Drug Discovery Platforms Tool Is Right for You?<\/strong><\/h2>\n\n\n\n
                                                                \n
                                                              • Solo researchers and academic labs:<\/strong> Lightweight or modular tools like ChemAxon or Cresset may be sufficient.<\/li>\n\n\n\n
                                                              • SMBs and biotech startups:<\/strong> Benchling or AI-focused platforms offer speed and collaboration.<\/li>\n\n\n\n
                                                              • Mid-market companies:<\/strong> Combining modeling tools with AI platforms balances depth and productivity.<\/li>\n\n\n\n
                                                              • Enterprises:<\/strong> BIOVIA or Schr\u00f6dinger provide scalability, compliance, and comprehensive coverage.<\/li>\n<\/ul>\n\n\n\n

                                                                Budget-conscious teams should prioritize usability and modular pricing, while premium users may value accuracy, AI depth, and enterprise support. Integration needs, regulatory requirements, and scalability should heavily influence final decisions.<\/p>\n\n\n\n

                                                                \n\n\n\n

                                                                Frequently Asked Questions (FAQs)<\/strong><\/h2>\n\n\n\n

                                                                1. What is a drug discovery platform?<\/strong>
                                                                A drug discovery platform is software that supports target identification, compound design, and optimization using computational and data-driven methods.<\/p>\n\n\n\n

                                                                2. Are AI-based platforms replacing traditional methods?<\/strong>
                                                                AI enhances but does not fully replace experimental and physics-based approaches.<\/p>\n\n\n\n

                                                                3. Can small biotech companies use these platforms?<\/strong>
                                                                Yes, many platforms scale well for startups and small teams.<\/p>\n\n\n\n

                                                                4. Do these tools support biologics?<\/strong>
                                                                Some do, but many focus primarily on small molecules.<\/p>\n\n\n\n

                                                                5. How long does implementation take?<\/strong>
                                                                From weeks for cloud tools to months for enterprise platforms.<\/p>\n\n\n\n

                                                                6. Are these platforms secure?<\/strong>
                                                                Most offer enterprise-grade security, but compliance varies.<\/p>\n\n\n\n

                                                                7. Do I need computational expertise?<\/strong>
                                                                Advanced platforms often require specialized skills.<\/p>\n\n\n\n

                                                                8. Can platforms integrate with ELNs and LIMS?<\/strong>
                                                                Yes, many support integration with broader R&D ecosystems.<\/p>\n\n\n\n

                                                                9. Are open-source alternatives viable?<\/strong>
                                                                They exist but often lack enterprise support and scalability.<\/p>\n\n\n\n

                                                                10. What is the biggest mistake when choosing a platform?<\/strong>
                                                                Overbuying complexity without aligning to actual research needs.<\/p>\n\n\n\n

                                                                \n\n\n\n

                                                                Conclusion<\/strong><\/h2>\n\n\n\n

                                                                Drug Discovery Platforms have become essential tools for accelerating innovation in pharmaceutical and biotech research. They reduce uncertainty, improve decision-making, and enable teams to move from idea to candidate more efficiently. The most important factors when choosing a platform are scientific fit<\/strong>, usability<\/strong>, integration<\/strong>, security<\/strong>, and long-term scalability<\/strong>.<\/p>\n\n\n\n

                                                                There is no single \u201cbest\u201d drug discovery platform for everyone. The right choice depends on your organization\u2019s size, budget, research focus, and technical expertise. By aligning platform capabilities with real-world needs, teams can maximize impact and bring better therapies to patients faster.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                Introduction Drug discovery is one of the most complex, time-consuming, and expensive processes in modern science. From identifying a promising biological target to validating a safe and… <\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[11138],"tags":[18089,18092,18096,18094,18087,18100,18088,18091,18098,18093,18090,18097,18099,18095],"class_list":["post-56536","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-ai-drug-discovery-software","tag-ai-in-drug-development","tag-biotech-drug-discovery-platforms","tag-cheminformatics-software","tag-computational-drug-discovery","tag-drug-design-platforms","tag-drug-discovery-platforms","tag-drug-discovery-software","tag-lead-optimization-tools","tag-molecular-modeling-software","tag-pharmaceutical-drug-discovery-tools","tag-structure-based-drug-design","tag-target-identification-software","tag-virtual-screening-platforms"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56536","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=56536"}],"version-history":[{"count":1,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56536\/revisions"}],"predecessor-version":[{"id":56538,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56536\/revisions\/56538"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=56536"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=56536"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=56536"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}