GPLACO SOLUTIONS delivers AI-driven drug discovery consulting for pharma and biotech leaders seeking faster asset selection, reduced attrition, and regulator-ready development pathways. We support sponsors and MAHs with computational drug discovery, target identification and validation, and early safety de-risking strategies aligned with FDA, EMA, PMDA, and ICH expectations. Our approach enables data-backed molecule prioritization, shortens time-to-IND, and prevents late-stage clinical failures—where over 90% of candidates are lost due to efficacy or safety gaps.
Drug discovery success today depends on early scientific rigor, predictive modeling, and regulatory foresight—not trial-and-error experimentation. GPLACO provides AI-driven drug discovery consulting for pharma and biotech partnering with executive teams to design discovery strategies that integrate biology, safety, and market intelligence from day one. Our advisory model ensures that only clinically and commercially viable molecules advance into development, protecting R&D investments and portfolio value.
Analyzing global safety data of existing products in the market to identify potential risks for new candidates before they enter the pipeline. Late safety failures are among the costliest risks in drug development. We integrate early safety signals, risk assessment, and target-related toxicity prediction to prevent clinical failures and regulatory rejections.
Advising investors on candidate selection by evaluating the molecular structure against known safety signals to ensure high clinical success rates.
Providing deep-dive safety assessments for venture capital and pharma investors to prevent high-cost failures in the clinical stage. This proactive Drug Discovery Risk assessment allows for 'safety-first' drug design, ensuring that Molecular Selection is informed by the most likely adverse event profiles. Our analytics provide the quantitative evidence investors need to justify large-scale capital allocation toward early-stage candidates.
Our Predictive Adverse Event Modeling utilizes advanced Drug Discovery Risk analytics to forecast potential safety outcomes before a molecule ever reaches a patient. By cross-referencing new candidates with real-world pharmacovigilance data from similar chemical classes, we identify 'off-target' toxicities and safety signals that traditional screening often misses.
Our AI analyzes untapped pharmacovigilance signal patterns to identify new therapeutic uses for existing molecules. This Drug Discovery Risk assessment identifies proven, safe compounds for new indications, significantly accelerating the development timeline while reducing the inherent 'trial-and-error' costs of early-stage molecular research.
Bridging the gap between early discovery and market reality by applying real-world safety data to early-stage toxicology models. Machine learning helps predict potential toxic effects of drug candidates, reducing the risk of adverse reactions during clinical trials.
Our predictive analytics replaces months of traditional manual data mining with rapid, automated biological assessments. By automating complex data mining, we don't just save time; we improve data integrity, which is a key requirement for successful regulatory submissions.
By using meticulous selection to identify safety-flawed candidates early, we prevent 'sunk-cost' scenarios. By automating complex data mining, we don't just save cost; we improve data integrity, which is a key requirement for successful regulatory submissions.
Our Risk modeling allows investors to bypass 'low-probability' molecules.
Our AI models adapt in real-time to new pharmacovigilance data, ensuring our molecule Selection advisory becomes more precise over time.
Partner with GPLACO Solutions to Accelerate Drug Development Initiatives and Shape the Future of Pharmaceutical Innovation
