Automations
This pillar focuses on R&D workflows that simulate and rank massive sets of chemical candidates against biological targets using graph models, generative chemistry, and virtual screening. The content should help pharma and biotech buyers see how a custom discovery workflow could shrink early-stage timelines, lower screening costs, and connect model outputs to lab validation and decision governance.
This foundational workflow automates the end-to-end process of generating, screening, and ranking millions of chemical compounds against biological targets, directly addressing the core pillar. It replaces manual library management and sequential simulation with a coordinated, agentic pipeline that integrates generative models, docking simulations, and property filters. The architecture connects cloud HPC resources, specialized AI agents for each screening step, and a decision engine to prioritize candidates for lab validation, significantly reducing early-stage R&D timelines and computational waste.
This workflow automates the critical bottleneck of sifting through virtual screening hits to identify true lead series with optimal properties. Specialized agents collaborate to analyze binding poses, cross-reference bioactivity databases, predict ADMET profiles, and assess synthetic feasibility. By orchestrating these analyses into a single, auditable decision pipeline, the system accelerates lead identification from weeks to days, improves the quality of selected series, and provides a defensible data trail for project teams and portfolio reviews.
This workflow automates the ideation of novel, patentable chemical matter by orchestrating generative AI models conditioned on target profiles, property constraints, and synthetic rules. It moves beyond one-off molecule generation to a continuous, goal-directed design cycle where agents propose structures, validate novelty against patent corpora, and score for synthesizability. The implementation ties generative models to a retrosynthesis planning engine and a project database, enabling chemists to explore a vastly larger design space and de-risk novel series before synthesis begins.
This advanced workflow automates the creation of entirely new molecular entities from scratch, guided by 3D target structure and multi-parameter optimization goals. It sequences generative graph models, 3D conformation builders, and binding affinity predictors in a closed loop, iteratively refining molecules toward ideal profiles. The architecture is built for high-throughput cloud execution, with rigorous novelty checks and integration into electronic lab notebooks (ELNs) to bridge in silico design with experimental validation, drastically shortening the initial ideation phase of drug projects.
This workflow automates the systematic exploration of chemical space around a promising scaffold to improve potency, selectivity, or properties while maintaining core IP. Agents use graph-based transformations, fragment libraries, and predictive models to generate analogs, rank them by multi-parameter scores, and propose synthesis routes. By automating this iterative optimization cycle, the workflow enables rapid series expansion, helps evade competitor patents, and provides data-driven recommendations for the next round of synthetic chemistry, improving medicinal chemistry efficiency.
This workflow automates the high-volume computational task of predicting and ranking protein-ligand binding affinities for massive virtual libraries. It orchestrates molecular docking engines, machine learning scoring functions, and molecular dynamics simulations on scalable cloud infrastructure. The system manages job queuing, result aggregation, and confidence scoring, delivering a prioritized list of candidates with estimated binding energies. This replaces error-prone manual batch processing, standardizes evaluation metrics across projects, and allows teams to focus computational resources on the most promising compounds.
This workflow automates the early-stage pharmacokinetic and toxicity profiling of candidate molecules, a major source of late-stage attrition. Specialized agents query and run predictions for absorption, distribution, metabolism, excretion, and toxicity using a suite of validated in silico models. The orchestration layer aggregates scores, flags critical liabilities (e.g., hERG inhibition), and generates a unified ADMET profile for each compound. Integrating this into the discovery funnel filters out problematic molecules before costly synthesis, improving the quality of the preclinical pipeline.
This workflow automates the detection and removal of promiscuous, assay-interfering compounds from virtual and real screening hits. It applies a multi-model agent that checks chemical structures against PAINS substructure libraries, analyzes assay readout patterns, and consults historical data on compound behavior. By embedding this automated gatekeeper early in the screening pipeline, teams avoid pursuing false-positive leads, saving months of wasted medicinal chemistry effort and focusing resources on truly druggable chemotypes.
This workflow automates the prediction of unintended interactions across a panel of biologically relevant off-targets (e.g., kinases, GPCRs). It uses agentic orchestration to submit candidate structures to multiple off-target prediction platforms, aggregate the results, and calculate selectivity scores. The system flags high-risk promiscuity patterns and generates a selectivity heatmap for project teams. This enables proactive mitigation of toxicity risks during lead optimization, reducing the likelihood of costly late-stage failures due to off-target effects.
This workflow automates the critical bridge between digital molecule design and physical synthesis by predicting feasibility and planning routes. An agent uses retrosynthesis AI to propose synthetic pathways, scores them for complexity, cost, and step count, and checks reagent availability against internal inventory systems. By providing synthesizability scores and draft routes alongside molecular designs, this workflow grounds virtual screening in practical chemistry, prevents the selection of unrealistic targets, and accelerates the handoff to synthesis teams.
This workflow automates the entire structure-based screening pipeline, from protein target preparation to pose analysis. It sequences tasks like binding site detection, ligand library preparation, parallelized docking runs across cloud HPC, and post-docking clustering and analysis. The architecture manages data flow between commercial and open-source tools (e.g., Schrodinger, OpenEye, AutoDock) and delivers a standardized results package. This turnkey automation reduces setup time from days to hours, ensures reproducibility, and allows computational chemists to manage larger, more diverse screening campaigns.
This workflow automates ligand-based approaches when a 3D protein structure is unavailable. Agents orchestrate similarity searching, pharmacophore modeling, QSAR prediction, and machine learning-based activity modeling against known active compounds. The system integrates data from public sources like ChEMBL, handles diverse molecular representations, and fuses scores from multiple methods to rank library compounds. This provides a powerful, automated complement to SBVS, expanding the scope of virtual screening projects and leveraging historical bioactivity data more effectively.
This workflow automates the monitoring and digestion of vast scientific literature to inform target selection and compound design. It uses LLM-powered agents to continuously scan PubMed and preprint servers, extract key entities (targets, diseases, compounds, mechanisms), and populate a structured knowledge graph. By automating literature surveillance, the system ensures discovery teams are alerted to the latest competitive intelligence and biological insights, reducing manual review burden and keeping project hypotheses current.
This workflow automates the search and analysis of global patent databases to assess freedom-to-operate and competitive IP threats for novel compounds. Agents parse patent claims and descriptions, extract chemical structures and biological claims, and map them against internal candidate libraries. The system generates IP risk reports and highlights white space for novel design. This automation accelerates due diligence, reduces legal review cycles, and helps guide medicinal chemistry away from infringing regions of chemical space.
This workflow automates the consolidation and stewardship of chemical data from disparate sources (internal synthesis, vendors, acquisitions) into a single, authoritative registry. It uses AI to standardize structures, deduplicate entries, validate associated data, and enforce metadata schemas. The orchestrated pipeline connects to ELNs, inventory systems, and screening databases, ensuring data integrity and providing a clean, queryable source of truth for all discovery activities, which is foundational for effective AI/ML model training.
This workflow automates the tedious process of mining internal assay reports and notebooks to build quantitative SAR models. NLP agents parse unstructured text and tables from PDFs and ELNs, extract compound structures, assay results, and experimental conditions, and populate a structured SAR database. This creates a continuously updated, machine-readable resource that powers predictive models for lead optimization, turning fragmented historical data into a strategic asset for guiding molecular design.
This workflow automates the fusion of genomics, proteomics, and transcriptomics data to identify and validate novel drug targets. It orchestrates bioinformatics pipelines for differential expression analysis, pathway enrichment, and network propagation, then applies machine learning to rank targets by druggability and genetic evidence. By automating this complex, multi-data-type analysis, the system accelerates target identification from months to weeks, provides a data-driven rationale for project initiation, and improves portfolio quality.
This workflow automates the mining of human genetic data (e.g., GWAS, CRISPR screens) to pinpoint causal genes for disease. Agents integrate data from public repositories like UK Biobank and DepMap, perform statistical fine-mapping, and assess target tractability using pharmacological databases. The automated pipeline generates ranked target lists with supporting genetic evidence, enabling discovery teams to build a pipeline with higher confidence in human disease relevance, thereby increasing the probability of clinical success.
This workflow automates the systematic discovery of entirely new biological targets beyond known disease genes. It uses multi-agent AI to analyze multi-omics datasets, scientific literature, and protein interaction networks to hypothesize novel disease-driving proteins or complexes. The system generates testable biological hypotheses and proposed experimental validation plans. This automation expands the target universe for discovery organizations, creating opportunities for first-in-class medicines and a more diversified pipeline.
This workflow automates the design of focused libraries for lead optimization by systematically exploring chemical space around multiple hit series simultaneously. Given a set of core scaffolds, agents generate analogs using rule-based and AI-driven transformations, filter for drug-like properties, and prioritize for synthesis. The orchestration manages the parallel design tracks, integrates results from predictive models, and outputs synthesis orders. This massively increases the throughput of analog design, allowing teams to explore more structural variations and converge on optimal leads faster.
This workflow automates the complex trade-off analysis required to advance a lead series, balancing potency, selectivity, ADMET, and physicochemical properties. It employs a scoring agent that applies customizable MPO algorithms, visualizes candidates in multi-dimensional property space, and recommends compounds that best meet the target product profile. By formalizing and automating this decision process, the workflow brings objectivity to lead optimization, reduces subjective debate, and helps teams select candidates with the highest overall probability of success.
This workflow automates the end-to-end process of designing a custom chemical library to probe SAR and improve a lead series. It integrates generative design, purchasable building block screening, and synthetic route feasibility checks. The system outputs a finalized list of compounds with associated vendor IDs or internal synthesis plans, ready for procurement or delegation to chemistry. This automation accelerates the often-manual library design process, ensuring libraries are diverse, synthetically accessible, and strategically targeted to answer key medicinal chemistry questions.
This workflow automates the prediction and iterative optimization of pharmacokinetic and pharmacodynamic parameters during lead optimization. Agents simulate ADME processes, predict tissue distribution, and model dose-response relationships using physiological-based PK/PD (PBPK) modeling. The system suggests structural modifications to improve half-life, bioavailability, or efficacy based on these simulations. This closes the loop between molecular structure and in vivo performance predictions earlier in discovery, de-risking candidate selection and reducing costly preclinical PK studies on suboptimal molecules.
This workflow automates comprehensive early safety assessment by predicting a broad panel of toxicological endpoints (e.g., mutagenicity, hepatotoxicity, cardiotoxicity). It orchestrates multiple in silico toxicity prediction platforms, aggregates scores, and flags compounds with severe liabilities. Integrated with the compound selection process, this acts as a safety gatekeeper, preventing the advancement of high-risk molecules and guiding chemists toward safer chemical space, ultimately reducing late-stage attrition due to toxicity.
This workflow automates the assessment of how a new drug candidate might interact with commonly co-prescribed medications. Agents predict CYP enzyme inhibition/induction profiles, assess transporter interactions, and simulate potential DDI scenarios using PBPK modeling. The system generates a risk report that informs clinical planning and can guide structural modifications to mitigate interaction risks. This proactive automation is crucial for designing safer drugs, especially for patient populations with polypharmacy.
This workflow automates the final, high-stakes decision gate where a lead series is narrowed down to a single preclinical candidate. It aggregates data from all prior assays, predictions, and experiments into a unified dashboard, applies portfolio-defined scoring rubrics, and facilitates collaborative review with audit trails. The orchestration ensures all required data packages are complete and standardized. This brings rigor and transparency to the candidate selection process, reduces committee meeting time, and creates a defensible record for regulatory and internal governance.
This workflow automates the core iterative engine of modern drug discovery, connecting in silico design, robotic synthesis, automated testing, and AI-driven analysis. Agents translate design ideas into synthesis instructions for robotic platforms, schedule biochemical assays, ingest results, and update predictive models to inform the next design cycle. This closed-loop automation dramatically accelerates the learning rate in lead optimization, reduces manual handoffs, and enables truly data-driven, high-throughput molecular engineering.
This workflow automates the planning and execution of chemical synthesis on robotic platforms. Given a target molecule, an agent plans a synthetic route, checks inventory for available starting materials, translates the procedure into platform-specific instructions, and schedules the run. It monitors the synthesis in progress and handles basic exception routing. This maximizes the utilization of expensive robotic assets, enables unattended synthesis of compound libraries, and frees up skilled chemists for more complex tasks.
This workflow automates the complex logistical task of designing assay plates for large-scale screening campaigns. It considers factors like compound concentration, solvent compatibility, control placement, and assay technology to generate optimized plate maps. The agent interfaces with liquid handling systems and compound management databases to translate the design into executable worklists. This automation eliminates manual, error-prone plate design, improves assay quality through optimal layout, and accelerates the setup of HTS campaigns.
This workflow automates the analysis and structuring of raw spectroscopic data to confirm compound identity and purity. AI agents process NMR and mass spectrometry files, predict spectra for proposed structures, compare predictions to experimental data, and assign confidence scores. The structured results are automatically filed in the ELN and compound registry. This drastically reduces the time analytical chemists spend on routine data interpretation, accelerates compound registration, and ensures data consistency.
This workflow automates the creation, optimization, and transfer of biological assay protocols between teams or sites. LLM agents analyze target biology and historical protocols to draft new assay procedures, then simulate or model expected outcomes. The system manages version control, generates SOP documentation, and facilitates review and approval cycles. This accelerates assay readiness for new projects, ensures reproducibility, and reduces the operational friction in scaling up screening efforts.
This workflow automates key steps in biologic discovery, such as antibody sequence optimization, affinity maturation, and developability profiling. It orchestrates agents that model protein-protein interactions, predict immunogenicity, and assess stability and expression levels. The system prioritizes candidate biologics for experimental testing. This brings AI-driven automation and scale to the traditionally empirical process of biologic drug discovery, expanding the searchable sequence space and improving the quality of lead candidates.
This workflow automates the complex, multi-component design of targeted protein degraders (PROTACs) and molecular glues. Agents model ternary complex formation between the target protein, E3 ligase, and linker, optimizing for binding cooperativity and degradation efficiency. The system also predicts physicochemical properties and synthesizability of these large, chimeric molecules. This specialized automation enables the rational design of degraders, a promising but challenging modality, reducing the trial-and-error approach and accelerating the discovery of viable candidates.
This workflow automates the design and safety assessment of covalent drug candidates. Agents identify potential covalent warheads, model their reactivity with target cysteine or other nucleophilic residues, and predict off-target reactivity with proteomes. The system ranks candidates based on a balance of potency and selectivity. This automation manages the unique risks of covalent drugs, enabling their deliberate and safe design rather than serendipitous discovery, opening up a broader range of druggable targets.
This workflow automates the specialized design and filtering for central nervous system (CNS) drugs, where crossing the blood-brain barrier (BBB) is paramount. It sequences predictive models for BBB permeability, efflux transporter substrate risk (e.g., P-gp), and CNS-specific toxicity. The orchestration layer applies these filters early in virtual screening and design cycles, ensuring only CNS-accessible candidates are prioritized. This focused automation increases the success rate in the challenging CNS therapeutic area by front-loading critical pathobiological constraints.
This workflow automates the discovery of novel oncology targets and rational drug combinations. Agents analyze tumor genomics data, synthetic lethal interactions, and immune cell infiltration patterns to propose new targets. For combinations, they model drug interaction networks, predict synergy/antagonism, and assess overlapping toxicity profiles. This system enables a more systematic, data-driven approach to oncology pipeline building, identifying high-potential single agents and combination regimens with a stronger mechanistic rationale.
This workflow automates the lifecycle management of the AI/ML models used throughout discovery. It monitors model performance for drift, triggers retraining pipelines when new experimental data arrives, validates updated models against hold-out sets, and manages versioned deployment to production environments. This ensures predictive models remain accurate and relevant as project data evolves, maintaining the reliability of AI-driven decisions and protecting the investment in discovery informatics infrastructure.
This workflow automates the operational monitoring of deployed AI models to ensure they remain fit-for-purpose. Agents track prediction distributions over time, compare them to baseline, and detect statistical drift that may indicate degrading performance. Upon detection, the system alerts data scientists and can trigger investigative or retraining workflows. This proactive monitoring is critical for maintaining trust in AI-driven discovery workflows, especially as they are used to make high-value candidate selection decisions.
This workflow automates the technical and governance processes for training AI models on distributed, sensitive datasets across partner organizations (e.g., in a pre-competitive consortium). It orchestrates secure federated learning cycles, managing encrypted model updates, aggregation, and redistribution without moving raw data. This enables the creation of more powerful, generalizable discovery models by leveraging broader datasets while strictly preserving intellectual property and patient privacy, a key requirement for industry collaboration.
This workflow automates the provisioning of complex computational chemistry and modeling tools to bench scientists through a conversational, self-service interface. Scientists describe a task (e.g., 'dock this compound to the new crystal structure'), and an agent orchestrates the backend software, runs the job on appropriate compute, and returns an interpretable report. This democratizes access to advanced modeling, reduces dependency on specialized computational teams, and accelerates hypothesis testing.
This workflow automates the audit and compliance requirements for AI models used in regulated discovery environments. It tracks model lineage, training data, hyperparameters, and performance validations, creating an immutable audit trail. The system manages approval gates for model promotion and integrates with electronic document management systems for regulatory inspection readiness. This governance layer is essential for deploying AI confidently in GxP or quality-critical discovery contexts, such as supporting regulatory submissions.
This workflow automates the discovery of novel agrochemicals by applying virtual screening and generative design to plant or pest biological targets. It incorporates specific property filters for environmental fate, toxicity to non-target species, and soil mobility. The orchestration connects to agricultural biology assay data and regulatory databases. This brings the efficiency of pharma-style AI discovery to the agrochemical industry, accelerating the development of safer, more effective crop protection agents with reduced environmental impact.
This workflow automates the design and screening of novel cosmetic ingredients for efficacy (e.g., anti-aging, moisturizing) and safety. Agents predict skin penetration, irritation potential, and stability, while also screening for desirable sensory properties. The system integrates with consumer preference data and regulatory lists of approved substances. This enables rapid, AI-driven innovation in the cosmetics industry, reducing reliance on traditional trial-and-error and helping to create differentiated, science-backed products.
This workflow automates the discovery of novel polymers and materials by applying molecular discovery principles to non-biological targets. It uses generative models for inorganic and polymeric structures, predicts material properties (strength, conductivity, glass transition temperature), and assesses synthetic pathways. This cross-industry application of the discovery workflow accelerates R&D for advanced materials used in electronics, packaging, and manufacturing, turning material design into a computational, goal-directed process.
This workflow automates the discovery of novel catalysts for chemical manufacturing. It screens virtual libraries of organometallic complexes or enzymes for catalytic activity, selectivity, and stability under process conditions. The system predicts reaction transition states and rates using quantum mechanical calculations orchestrated at scale. This application of discovery automation to catalysis can revolutionize chemical production by identifying greener, more efficient catalysts, reducing the time and cost of process development.
This workflow automates the business intelligence layer of discovery, calculating the projected ROI of different pipeline candidates and portfolio strategies. It ingests data on development costs, probability of success (PoS) estimates, market size, and competitive landscape to run financial simulations. The system provides dashboards that help R&D leadership prioritize resources, kill underperforming projects earlier, and maximize the economic value of the discovery portfolio, directly linking scientific work to business outcomes.
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One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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