Pharmaceutical

Optimizing the Tests of Molecules - Clinical Phases

Accelerated Development of Models

Get precise, explainable predictions on molecule efficacy and safety, expediting the process of drug development.

Resource Allocation on Most Promising Molecules

Make the most of your research budgets by focusing only on the most promising molecular candidates, as guided by our self-explainable models.

Regulatory Compliance

Simplify the process of clinical validation by providing regulators with transparent and data-backed findings, aiding in quicker approvals.

Enhanced Subject Selection

Gain clear insights into selecting optimal subjects for your clinical trials, minimizing patient dropouts.

Optimizing the Discovery of Molecules - Biomarkers

Targeted Discovery

Use explainable AI to identify and focus on molecular structures that are most likely to result in effective biomarkers.

Risk Mitigation

Understand the underlying factors affecting the success or failure of biomarker identification, allowing for more informed investment decisions.

Collaborative Innovation

The explainability feature enables seamless collaboration between data scientists, biochemists, and other stakeholders.

Analysis of Sales Performances

Insight-Driven Marketing

Pinpoint exactly what factors are driving or impeding sales, providing actionable insights for marketing strategies.

Cost Efficiency

Optimize inventory and distribution strategies by understanding the variables that impact sales, thereby reducing overhead costs.

Consumer and Customer Trust

Build trust with stakeholders and clients by offering transparent metrics and predictions on molecule sales performance.

Guided Production Cycle

Leverage your sales insights to optimize production planning, avoiding both stockouts and expired inventory.

Analysis of Patients Issues with Molecules

Enhanced Patient Safety

Xpdeep's self-explainable algorithms can identify potential adverse effects or contraindications that might be otherwise overlooked.

Personalized Treatment of Patients

Gain insights into how different patient profiles interact with various molecules, enabling more personalized treatment plans.

Compliance and Accountability for Ethical Treatment

Ensure ethical treatment of patient data and bolster compliance efforts with a transparent decision-making process.

QA, QC and Manufacturing

In-Line Quality Regulation to Reduce Wastage

Deploy explainable deep learning models to identify weak points in the production line, facilitating proactive measures to reduce wastage.

Accelerating AI Corrections for Cost Savings

Precise and explainable predictions streamline the quality-check process, leading to significant time and resource savings on corrections.

Resource Optimization through Explainability

Detailed explanations of predictive outcomes enable the efficient allocation of maintenance resources to where they are most needed.