Leveraging Gen AI to Predict ADMET and Prevent Off-Target Toxicity

How Generative AI is Transforming Drug Discovery Workflows and Project Management for Early Risk Prediction

This blog is part of the “GenAI in Healthcare Report 2025” by Murali Sudram in collaboration with Pitchworks VC Studio. The report explores how generative AI is reshaping scientific research, clinical workflows, and drug discovery. Stay tuned for more in-depth explorations of real-world applications and enterprise adoption strategies.You can download our Gen AI in Healthcare report from here https://www.pitchworks.club/healthcaregenaireportIf you are into manufacturing, you can download our Gen AI manufacturing report here https://www.pitchworks.club/gen-ai-manufacturing-report-2025

Predict ADMET and Prevent Off-Target Toxicity

With late-stage failures still costing billions, the integration of Generative AI (Gen AI) is revolutionizing how teams approach this challenge. By analyzing molecular structures, historical data, and biological pathways, Gen AI enables faster and more accurate ADMET predictions early in the pipeline. This blog explores how Gen AI is being embedded into R&D workflows, the evolving tool stack used by scientists, data engineers, and project managers, and how each stakeholder—from medicinal chemists to clinical strategists—can harness AI to reduce risk, flag off-target toxicity, and streamline decision-making in real time. It also includes a case study demonstrating how a Gen AI-powered ADMET workflow was successfully implemented, highlighting tangible improvements in prediction speed, risk assessment, and project coordination.

absorbed, distributed, metabolized, and excreted in the body

Predicting ADMET and Preventing Off-Target Toxicity refers to evaluating a drug candidate’s pharmacokinetic behavior—how it's absorbed, distributed, metabolized, and excreted in the body—along with identifying any potential toxic effects caused by unintended interactions with biological targets. This process is typically done during the early discovery and preclinical stages of drug development, ideally even before a molecule is synthesized at scale. According to a 2022 report by the FDA, over 30% of clinical trial failures are linked to poor ADMET properties, while 20% fail due to unexpected toxicity—many of which could have been identified earlier with better prediction models.

According to Deloitte’s 2023 pharma R&D benchmarking report, every 10% improvement in early-stage candidate selection could lead to a $100–150 million reduction in downstream R&D costs.

Key players in the procees include computational chemists, medicinal chemists, DMPK scientists (Drug Metabolism and Pharmacokinetics), data scientists, and project managers. Today, building and validating ADMET profiles for a single compound can take 6–18 months, depending on the tools and testing stages involved. Identifying off-target toxicity requires running simulations or screening against databases of known protein targets—often a time-consuming step. Gen AI can significantly compress this cycle by learning from millions of compounds, simulating interactions, and flagging risks in days instead of weeks.

Accelerating ADMET and toxicity prediction has a powerful ripple effect across the drug discovery pipeline.

The ADMET prediction process follows a structured workflow that combines computational modeling, laboratory validation, and cross-functional collaboration. Each stage involves specialized experts who contribute to evaluating a compound's pharmacokinetics and safety profile before it moves into clinical trials. The table below outlines the key steps, stakeholders, and actions involved in streamlining ADMET assessment and preventing off-target toxicity.

Stage	Who Is Involved	What Happens
1. Compound Design & Selection	Medicinal Chemists, Computational Chemists	Design or select candidate molecules based on target interaction and properties.
2. In Silico ADMET Prediction	Data Scientists, Cheminformaticians, AI Engineers	Use Gen AI and QSAR models to predict ADMET profiles from molecular structure.
3. In Vitro Screening	Lab Scientists, DMPK Teams	Conduct lab-based assays (e.g., Caco-2, microsome stability) to validate models.
4. Off-Target Toxicity Screening	Bioinformaticians, Toxicologists	Predict and simulate unintended binding with known off-target proteins.
5. Data Integration & Analysis	Data Analysts, Project Managers, AI Platforms	Aggregate prediction results, assay data, and toxicology risk into dashboards.
6. Candidate Prioritization	Discovery Leads, Clinical Strategists	Rank compounds based on ADMET safety and efficacy; deprioritize risky ones.
7. Preclinical Validation	Pharmacologists, Animal Testing Teams	Confirm ADMET predictions with animal models before clinical entry.

Gen AI not only enhances the accuracy of predictions but also improves cross-functional collaboration—integrating results across tools, labs, and teams. This shift allows companies to kill bad candidates early, prioritize promising ones, and ultimately reduce time-to-market by up to 25%.

The ADMET Gen AI workflow and application featured in this blog were developed entirely in-house at Pitchworks Gen AI Labs in collaboration with the Pitchworks Global Capability Center (GCC). The Labs focus on R&D and foundational model tuning for life sciences, while the GCC acts as an innovation engine that enables productization, cross-functional collaboration, and deployment at scale across global pharma and biotech partners.

Kwapio is Portfolio Pitchworks, which acts like professional services automation for drug development and GCC- global competency center. The Project was deployed with Pitchworks GCC and Customers of of it which is also a well kown Life sicince GCC. Pitchworks GCC serves as a dedicated hub where data scientists, medicinal chemists, AI engineers, regulatory experts, and program managers co-create AI-powered tools, ensuring they’re compliant, auditable, and tailored for real-world R&D environments. The implementation of the Gen AI ADMET platform reflects the studio’s commitment to reducing drug development timelines, improving early predictive accuracy, and enabling data transparency across teams.

Target Users:

• Medicinal Chemists: Rapid feedback on new compounds.

• Preclinical Biologists: Prioritize molecules with fewer off-target risks.

• Program Managers: Visualize tasks, timelines, and scientific outputs in one view.

• Clinical Strategists: Early go/no-go signal for high-risk molecules.

• AI & Data Science Leads: Validate, tune, and track model performance.
  

Target Companies:

• Mid-sized Biotech Firms (50–500 employees): Lacking large in-house data science teams but needing predictive support.

• Global Pharma R&D Units: Seeking faster iteration with explainable AI.

• Healthtech Service Providers: Offering computational chemistry or ADMET as-a-service.

• Preclinical CROs: Looking to embed AI prediction layers into their validation pipeline.

By embedding Gen AI into both the scientific and project management layers of ADMET workflows, Pitchworks enables faster, safer, and more collaborative drug development—bringing precision and agility to a traditionally slow and fragmented process.

Kwpaio is an intelligent project management platform designed for complex, multi-stakeholder R&D workflows. In an ADMET prediction project, it helps streamline task tracking, automate workflows, and integrate diverse scientific tools and teams into a single collaborative environment.

How Kwapio Automates the Complete Workflow

7 Stages of ADMET Prediction Project Management in Kwpaio

1. Project Scoping & Objective SettingDefine project goals (e.g., predict ADMET for X compounds), success criteria, timelines, and stakeholders.

2. Data Collection & PreparationCurate chemical structures, historical ADMET data, and clean datasets for modeling and validation.

3. Tool & Workflow SetupChoose and integrate Gen AI platforms, QSAR tools, and databases into a unified prediction pipeline.

4. Model Development & SimulationTrain or fine-tune AI models to predict ADMET properties and off-target toxicity based on input data.

5. Experimental ValidationPlan and execute in vitro/in vivo assays to validate AI predictions and confirm biological relevance.

6. Analysis & Decision-MakingCompare model outputs with assay results, flag high-risk compounds, and prioritize drug candidates.

7. Reporting & HandoffDocument findings, generate dashboards or reports, and share results with R&D or clinical teams.

   A Task Type dropdown in project management tools allows teams to categorize work items (tickets) by their nature—Task, Subtask like Research, Data, Development, etc. This helps in:

   • Structuring work for better tracking and reporting

   • Assigning relevant teams/persons based on expertise

   • Filtering and prioritizing tasks efficiently across stages

   
   

   

   Set up a custom task and add more context to the task

Function	How Kwpai helsp
Task & Timeline Management	Automates assignment of tasks across chemists, data scientists, and lab teams with deadlines and dependencies.
Workflow Automation	Triggers next steps (e.g., move from in silico to in vitro) automatically based on project status.
Integration Hub	Connects seamlessly with cheminformatics tools like KNIME, Schrödinger, RDKit, and data stores like ELNs, LIMS.
Real-Time Dashboard	Displays prediction accuracy, compound status, off-target alerts, and assay progress in one place.
Cross-Team Collaboration	Enables structured communication between computational, experimental, and management teams.

How Kwapio Integrates with Existing Tools and Stakeholders

Tool / Stakeholder	Role in ADMET Workflow	Kwapio Integration Function
KNIME / Pipeline Pilot	Data cleaning, feature engineering for models	Kwapio triggers pipelines and logs outputs automatically.
RDKit / Schrödinger / MOE	Molecular modeling and structure-based prediction	Auto-launches predictions and stores results in the dashboard.
LIMS / ELN Systems	Experimental assay management and data storage	Syncs results into Kwapio timelines and flags validation gaps.
Project Managers & Leads	Oversee progress, deadlines, and regulatory readiness	Gets alerts on risk, task slips, and upcoming decisions.
Toxicologists / DMPK Scientists	Review off-target risks and ADME scores	Custom views for reviewing AI output with lab data.

Kwapio is a smart project orchestration platform designed for scientific R&D workflows. In the context of ADMET prediction, Kwapio acts as a central command system that connects task management, data pipelines, model execution, and team collaboration—all while integrating with Gen AI tools to drive faster, more accurate insights.

Kwapio directly connects to Gen AI tools through APIs, notebooks, or integrated platforms, allowing model execution and monitoring without switching contexts. Supported integrations include

Gen AI Model / Platform	Function in ADMET Prediction	Kwapio Role
MolBERT / ChemBERTa	Uses transformer models to encode molecular SMILES and predict properties	Kwapio triggers model runs and fetches ADMET scorecards.
Graph Neural Networks (GNNs)	Models molecular graphs to predict toxicity, solubility, etc.	Executes batch inference jobs and routes flagged compounds.
DeepTox / TOX21 AI Models	Specialized toxicity prediction using public datasets	Auto-compares model results with in vitro outcomes.
Custom QSAR Models (via Python, Jupyter)	Predicts ADMET based on historical compound data	Schedules training, logs outputs, and notifies reviewers.

How Gen AI Helps in ADMET Prediction

• Structure-Based Prediction: Gen AI models analyze SMILES, graphs, or 3D molecular structures to predict how a drug behaves in the body—absorption rate, metabolic stability, likelihood of crossing the blood-brain barrier, and more.

• Toxicity Flagging: These models also simulate interactions with a broad protein database to flag off-target toxicity, reducing the chance of downstream failure.

• Speed & Scale: Instead of manually validating a few compounds, Gen AI allows prediction for thousands of molecules within hours, dramatically shortening the R&D cycle.

Predicting ADME(T) Properties. Pipelines

In the Kwapio ADMET project management dashboard, the Kanban view provides a visual and interactive layout of all tasks (tickets) across the drug prediction and validation pipeline. Each card or ticket represents a unit of work—such as "Validate CYP450 Interaction" or "Review Cytotoxicity Assay Results"—and is organized under clear stages like Scoping, Data Collection, Simulation, Experimental Validation, and Reporting. Every ticket includes key fields: Acceptance Criteria (e.g., deviation thresholds, assay standards), Description, Estimated Hours, Actual Hours Tracked, Priority Level, and Tags for filtering (e.g., #in-vitro, #toxicology, #AI-discrepancy). The Kanban layout allows users to drag and drop tickets between stages, ensuring real-time progress visibility and accountability.

tracking all Acceptance Criteria inform a ticket page

This view is deeply integrated with other modules—data results, model predictions, lab reports—so that each task stays connected to relevant context. For example, a ticket tagged with #solubility can automatically fetch predicted vs. lab values and show them inline. Having acceptance criteria embedded directly into tickets ensures clarity on success benchmarks before tasks start. The ability to track hours helps in effort estimation vs. reality, and high-priority tasks (e.g., flagged model mismatches) can be easily escalated. This structure enhances cross-functional coordination, improves throughput, and ensures every validation task is both scientifically rigorous and operationally traceable.

Ai dashboard for ADMET drug discovery

In the Kwapio platform, Gen AI integration plays a central role in predicting ADME(T) properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) from early-stage compound structures. As soon as a molecule is submitted via SMILES, 2D/3D structure, or graph input, Gen AI models instantly generate predictions for key parameters like permeability, BBB penetration, solubility, metabolic liability, and cytotoxicity risks. These outputs are not static—each prediction is versioned, timestamped, and linked directly to the corresponding task in the project Kanban board, ensuring traceability and audit readiness.

Kwapio could save 127 hours in time

What’s powerful is how this prediction workflow is fully integrated into the Kwapio project dashboard. For each molecule or batch, the dashboard shows real-time side-by-side comparisons of Gen AI predictions versus lab results (as they are uploaded), flagging major deviations with risk scores. This enables scientists to track which properties need revalidation, which molecules are ready for preclinical progression, and where the AI model might require retraining.

toxicity flag if lwo risk or high risk with flog of proten target coming form model

Users can also drill down from any Kanban card to view detailed prediction rationale, model confidence scores, and links to assay reports—making AI-driven decisions explainable, actionable, and aligned with experimental workflows.

As drug discovery becomes increasingly data-driven, Gen AI is reshaping how teams operate across the R&D spectrum—from molecular design to clinical prioritization. This blog showcased how AI isn't just automating predictions but enabling smarter, faster, and more transparent decision-making by embedding itself into everyday workflows. Whether it's a medicinal chemist validating compound permeability or a clinical strategist assessing development risk, AI is now a core collaborator.

The successful implementation of the Kwapio ADMET platform, developed within Pitchworks Gen AI Labs and GCC, demonstrates the power of aligning predictive science with structured project management. By integrating AI-powered outputs into a live Kanban system, Kwapio not only improved prediction accuracy and speed but also enhanced cross-team collaboration and accountability. For biotech startups, CROs, and global pharma teams alike, this marks a blueprint for how AI tools like Kwapio can future-proof their R&D operations.