Pharmaceutical & Biotech R&D
with Purpose-Built AI
As 90% of drug candidates fail during development with an average costs exceeding $2.6 billion per approved drug, the industry needs transformative solutions. Our AI/ML platform is purpose-built for pharmaceutical R&D, delivering validated predictions that accelerate timelines, reduce attrition, and optimize resource allocation.
We partner with pharmaceutical, biotech and other companies involved in innovation and discovery to de-risk early-stage programs through precision-engineered AI/ML capabilities.
Precision Predictions
State-of-the-art models trained on diverse pharmaceutical datasets with >80% accuracy on validation
Rapid Deployment
2-8 months pilots showing defined success metrics and go/no-go decision points every quarter
Enterprise Security
On-premise, private cloud, or FedRAMP-certified deployment with full data sovereignty
Hit-to-Lead & Lead Optimization
Hit-to-Lead Optimization with Predicted ADMET Profiles
Accelerate hit-to-lead timelines. Our platform predicts solubility, permeability, metabolic stability, hERG liability, and plasma protein binding while generating 500-1,000 optimized analogs per scaffold that maintain target affinity.
Key Deliverables
- Multi-parameter ADMET predictions (>70% accuracy on validation sets)
- 500-1,000 optimized analog designs per hit scaffold
- Explainable AI outputs with structural feature analysis
- Integration with Schrödinger and BIOVIA platforms
- Weekly progress reviews with medicinal chemistry teams
Toxicophore Identification and Mitigation
Prevent late-stage failures from idiosyncratic toxicity. Identify novel structural alerts for hepatotoxicity, cardiotoxicity, and other liabilities beyond standard PAINS filters, then generate analogs that eliminate toxicophores while preserving efficacy.
Key Deliverables
- Novel toxicophore identification with mechanistic hypotheses for risk mitigation
- 100-200 detoxified analogs per problematic lead
- Counterfactual explanations pinpointing toxic moieties
- Reactive metabolite prediction and mitigation strategies
Multi-Parameter Optimization Using Generative AI
Navigate the complex trade-offs in medicinal chemistry by optimizing 5-7 parameters (potency, selectivity, solubility, permeability, metabolic stability) while constraining synthetic accessibility, delivering focused libraries of 50-100 compounds for parallel synthesis.
Key Deliverables
- Reinforcement learning for multi-objective optimization
- Pareto front visualization of parameter trade-offs
- Synthetic accessibility scoring (RA score <6)
- Bi-weekly design cycles with experimental feedback
Drug-Drug Interaction Prediction
Identify DDI liabilities early in lead optimization. Predict CYP450 inhibition/induction and transporter interactions with >80% classification accuracy, then suggest structural modifications to reduce DDI risk while maintaining target activity.
Key Deliverables
- CYP inhibition predictions (1A2, 2C9, 2C19, 2D6, 3A4)
- Transporter interaction predictions (P-gp, BCRP, OAT, OCT, MATE, OATP)
- Clinical DDI magnitude estimates using mechanistic static models
- Structural modification recommendations
- Retrospective analysis of terminated programs
Blood-Brain Barrier Penetration Prediction
Optimize CNS exposure for central nervous system therapies or minimize brain penetration for peripherally targeted drugs. Our models predict BBB permeability, P-gp/BCRP efflux, and brain-to-plasma ratios beyond simple physicochemical rules, with SAR guidance for achieving target CNS profiles.
Key Deliverables
- BBB permeability predictions with confidence intervals
- P-gp and BCRP efflux liability assessments
- Unbound brain-to-plasma ratio (Kp,uu) predictions
- SAR analysis identifying CNS penetration drivers
Novel Modalities & Target Classes
De Novo Design of PROTACs for Undruggable Targets
Unlock transcription factors and other “undruggable” targets through AI-designed PROTAC molecules. Our platform predicts ternary complex formation, optimizes linkers, and estimates cellular degradation efficiency for VHL-, CRBN-, and IAP-based degraders.
Key Deliverables
- 20-30 PROTAC designs per target with predicted DC50/Dmax
- Ternary complex stability predictions and geometry optimization
- Proteome-wide off-target degradation predictions
- Cell permeability models for 700-1,200 Da PROTACs
- Iterative refinement based on experimental data
Generative AI for Macrocycle Design (Beyond RO5)
Access intracellular protein-protein interactions with membrane-permeable macrocycles. Our specialized beyond-RO5 AI predicts chameleonicity, passive permeability, and oral bioavailability for 12-20 members ring macrocycles with N-methylation optimization.
Key Deliverables
- 100-150 macrocycle designs per intracellular target
- Conformational analysis for membrane permeation
- N-methylation pattern optimization
- Predicted PAMPA, RRCK permeability and oral bioavailability
- Integration with Schrödinger Maestro and MOE
AI-Guided Covalent Inhibitor Design
Design potent, selective covalent drugs with optimized warhead-linker combinations. Our platform predicts covalent binding kinetics (kinact/KI), assesses proteome-wide selectivity, and suggests reversible covalent scaffolds for temporal target modulation.
Key Deliverables
- 50-100 covalent inhibitor designs per target
- Warhead selection for Cys, Ser, Lys, Tyr nucleophiles
- Quantum mechanical calculations for reactivity
- Proteome-wide off-target modification predictions
- Validation with mass spec and cellular assays
Generative AI for Peptide Therapeutics
Design metabolically stable, cell-penetrating peptides for extracellular and intracellular targets. Our platform optimizes protease resistance through D-amino acids and N-methylation, predicts MHC-II immunogenicity, and designs stapled peptides with helical stability.
Key Deliverables
- 30-50 peptide designs per target (linear, cyclic, stapled)
- Protease cleavage site predictions and stabilization strategies
- Cell permeability models for peptide modalities
- T-cell epitope predictions and deimmunization
- Serum stability and functional validation
Protein Degrader Selectivity & Pharmacology Prediction
De-risk TPD programs by predicting proteome-wide degradation selectivity, neo-substrate formation, and hook effects (dose-response curve anomalies at high concentrations). Our models estimate Dmax, DC50, and duration of action while identifying off-target degradation risks for PROTACs and molecular glues.
Key Deliverables
- Proteome-wide selectivity predictions for 20+ degraders
- Neo-substrate identification for molecular glues
- Dose-response predictions including hook effects
- AlphaFold-Multimer ternary complex modeling
- Validation with quantitative proteomics (TMT-MS)
RNA-Targeted Small Molecule Design
Pioneer RNA-targeted therapeutics for repeat expansions, riboswitches, and UTR structures. Our AI generates small molecules with predicted RNA binding affinity, selectivity versus off-target RNAs, and cellular activity for splicing/translation modulation.
Key Deliverables
- 50-100 RNA-targeting small molecule designs per target
- RNA structure prediction and binding mode analysis
- Selectivity predictions against off-target RNAs and DNA
- Nuclear/cytoplasmic distribution predictions
- Validation with SPR, ITC, and cellular reporters
DMPK & Development Optimization
Predictive Modeling of Clinical PK from Preclinical Data
Improve IND candidate selection with accurate human PK predictions. Our models predict clearance, Vd, half-life, and bioavailability from preclinical data with <2-fold error for clearance, incorporating species-specific differences and non-linear PK flags.
Key Deliverables
- Human PK predictions with confidence intervals
- Species scaling with transfer learning frameworks
- First-in-human dose recommendations
- Non-linear PK and food effect flags
- Integration with GastroPlus and Simcyp PBPK
Solid Form Selection & Formulation Development AI
Accelerate IND timelines by 6-12 months through AI-guided solid form selection. Predict polymorph landscapes, salt/co-crystal formation, biorelevant solubility, and formulation performance to narrow experimental screening from 50+ to 10-15 conditions.
Key Deliverables
- Crystal structure prediction and polymorph stability rankings
- Salt and co-crystal formation propensity with counterions
- Solubility predictions in FaSSIF, FeSSIF across pH
- Formulation strategy recommendations (IR, MR, ASD, lipid)
- Validation with XRPD, DSC, and mini-formulation studies
Oral Drug Absorption Prediction & Optimization
Address the 40% of candidates with <30% oral bioavailability. Our PBPK-ML hybrid predicts fraction absorbed, first-pass extraction, and food effects, then recommends enabling formulations (salts, particle size, lipid, ASD) to improve absorption.
Key Deliverables
- Human bioavailability predictions with uncertainty quantification
- Mechanistic analysis of absorption-limiting factors
- Formulation recommendations for poorly soluble compounds
- Dose-proportionality predictions
- Integration with GastroPlus PBPK workflows
Target Identification & Validation
AI-Guided Fragment-Based Drug Design
Accelerate FBDD campaigns by approximately 4+ months through AI prioritization of fragment hits and elaboration strategies. Our platform predicts fragment growing/linking with FEP-level accuracy and generates focused libraries of 500-1,000 compounds from crystallographic hits.
Key Deliverables
- Fragment hit prioritization by ligand efficiency and developability
- Top 20 elaboration strategies per fragment with predicted ΔΔG
- Fragment linking with optimal linker geometries
- Integration with XChem crystallography pipeline
- Approximately 30% of elaborations showing >10-fold affinity gains.
NLP for Automated Literature Mining & Target Validation
Compress 3-6 months of target validation into weeks. Our NLP platform extracts disease-gene associations, genetic evidence, drug ability assessments, and safety liabilities from millions of publications, automatically creating comprehensive target validation packages.
Key Deliverables
- Comprehensive target validation packages for 10 novel targets
- Genetic evidence scoring (GWAS, UK Biobank, gnomAD)
- Drug ability assessment with known ligands and structures
- Safety evaluation from knockout phenotypes and expression
- Continuous monitoring for emerging evidence
AI for Phenotypic Screening Hit ID & Target Deconvolution
Unlock phenotypic screening value through deep learning analysis of high-content imaging, mechanism-of-action clustering, and target prediction via chemical proteomics data integration. Identify phenotypic hits and elucidate mechanisms 2-3x faster than manual analysis.
Key Deliverables
- Deep learning analysis of Cell Painting and Opera Phenix data
- Phenotypic clustering and mechanism-of-action predictions
- Target predictions from chemical proteomics integration
- Chemical probe recommendations for validation
- Integration with Columbus image analysis and CDD Vault
Biologics & Antibody Engineering
Medicinal Chemistry Productivity Tools
Drug Repurposing & Combination Therapy
Pharmaceutical AI Specialists
Our team combines PhD-level drug discovery expertise with deep learning engineering. We understand medicinal chemistry SAR, DMPK principles, and regulatory requirements and the machine learning theory.
Validated Performance Metrics
We don’t just promise accuracy – we prove it. All our models are benchmarked against industry-standard datasets and your proprietary data. We provide detailed validation reports with confidence intervals, error analysis, & applicability assessments.
Cross-Domain
Connects chemical, clinical, and commercial signals.
Reduces asset discovery timelines from months to hours.
Want to see a demo or
got a question, idea, or partnership in mind?
Copyright © 2025 Prognica Labs, All Rights Reserved
Trust & Security || Terms of Use || Privacy Policy || Cookies Policy