At Pharmaeconomica, our core capabilities in AI, computational chemistry, and peptide science drive breakthroughs in drug discovery. Our expertise accelerates the identification of novel therapeutic compounds, reducing drug development costs and timelines significantly.
One standout aspect of our capabilities lies in peptide-based drug discovery. We maintain an extensive in-house database of peptides and employ robust methodologies to incorporate non-canonical amino acids, enhancing specificity and potency. This approach provides unique insights into therapeutic development, allowing us to design peptide-based drugs with improved stability and bioavailability.
Advanced AI and Machine Learning methodologies for effective hit identification
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We harness the power of AI/ML algorithms to uncover hidden patterns.
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We search diverse chemical space to discover potent novel molecules with accelerated timelines and reduced costs.
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ML-driven virtual-HTS discovers novel, drug-like molecules and easily synthesizable molecules with high activity success, distinct from initial datasets.
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vHTS-trained ML models can create generative designs to expedite hit-to-lead and lead optimization.
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We compile relevant data, process and engineer features, apply machine learning models, assess performance, and ensure robustness through validation techniques. We also use machine learning to identify compounds similar to leads and validate findings through molecular docking simulations.
Comprehensive In-House Compound Database
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Diverse Chemotypes for Screening.
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Our library houses approximately 4 billion molecules.
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We maintain a curated sublibrary of 50 million drug-like molecules ( good ADMET, and PAINS free).
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Our pioneering proprietary small peptide library, acknowledged in Ahmad et al. 2023, sets new standards.
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We identify high-affinity cyclic peptides for any target and modify them with unnatural amino acids using a semi-automated process.
Highly Integrated Discovery Pipeline
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Constructing precise protein structures in the absence of experimental data.
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Employing refined algorithms and machine learning to distinguish actives from decoys.
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Employing consensus docking and clustering to streamline ligand analysis.
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Discovering promising chemical scaffolds with high affinity and selectivity for particular targets.
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Ensuring precise binding affinity predictions for both membrane-embedded and soluble protein-ligand complexes.
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Conducting MD simulations on top hits to evaluate their stability across diverse protein environments.
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​Utilizing rigorous physics-based FEP calculations to accurately predict binding affinity for top hits.
