PharmaAI
20 December 2025 2025-12-22 13:59PharmaAI
How Cheminformatics & Computational Drug Discovery Services Can Help Your Need
Services Category
Target-to-hit & hit-to-lead
Cheminformatics & AI/ML Drug Discovery Services
Cheminformatics and AI-driven drug discovery services for pharma and biotech. Independent expert support to transform chemical data into actionable decisions across hit discovery, lead optimization, and ADMET risk reduction.
Book a 30-Minute Discovery Call: +1(571)355-3767
and Request a Scoped Proposal
Core Cheminformatics Services
1. Chemical Data Curation & Preparation
Scope
• Structure standardization (salts, tautomers, stereochemistry)
• SMILES/InChI normalization and deduplication
• Assay data cleaning and aggregation
• Outlier detection and error correction
Deliverables
Audit-ready curated datasets
Reproducible ETL pipelines (Python/RDKit)
Data quality reports and documentation
2. Chemical Space Analysis & Library Profiling
Scope
• Molecular fingerprints (ECFP/Morgan, MACCS)
• Similarity searching and clustering (Butina, hierarchical, k-means)
• Bemis–Murcko scaffold analysis
• Chemical diversity and redundancy assessment
Deliverables
Chemical space maps and diversity metrics
Cluster representatives and acquisition recommendations
Scaffold-level SAR insights
3. QSAR & Machine Learning Model Development
Scope
• Descriptor engineering (physicochemical, topological, 2D/3D)
• Fingerprint-based and hybrid feature sets
• ML algorithms: Random Forest, XGBoost, SVM, regression & classification
• Hyperparameter tuning and feature selection
Deliverables
Validated QSAR models
Cross-validation and external test performance
Model cards with assumptions and limitations
Reproducible notebooks or deployable APIs
4. ADMET & PK Prediction
Endpoints
• Solubility, permeability, pKa, logP/logD
• Metabolic stability and clearance
• Toxicity flags (hERG, CYP, DILI, AMES)
• Bioavailability and PK-related properties
Deliverables
Property predictions with confidence estimates
Applicability domain analysis
Risk flags with medicinal chemistry guidance
Data Engineering & Analytics for R&D
• SQL-based data warehouses
• Versioned datasets and audit trails
• Power BI / SSRS dashboards
• Assay performance and QC
• Portfolio and compound progression views
• Interactive dashboards
• KPI definitions
• SOPs and documentation
Computational Chemistry & Structure-Based Drug Design Services
Structure-based drug design and molecular simulation services to accelerate hit-to-lead decisions –Docking, molecular dynamics, and structure-based insights—delivered with medicinal chemistry context.
How Computational Chemistry Supports Discovery
Computational chemistry reduces experimental cost by:
• Prioritizing compounds before synthesis
• Explaining SAR trends structurally
• Identifying binding liabilities early
• Improving confidence in lead selection
Core Computational Chemistry Services
1. Molecular Docking & Virtual Screening
Scope
• Protein preparation (protonation, waters, cofactors)
• Grid generation and flexible docking
• Tools: Schrödinger Glide, AutoDock Vina, MOE
• Consensus and rescoring strategies
Deliverables
Ranked compound lists
Binding poses and interaction maps
Enrichment metrics and false-positive analysis
Red-flag alerts for strained or unstable poses
2. Structure-Based Virtual Screening (SBVS)
Scope
• Focused or large-scale library screening
• Physicochemical and drug-likeness filters
• Scaffold-aware prioritization
Deliverables
Top-ranked compounds with rationale
Pose galleries and contact fingerprints
Acquisition and synthesis recommendations
3. Molecular Dynamics (MD) Simulations
Scope
• Short MD runs for pose stability assessment
• RMSD, RMSF, hydrogen bond persistence
• MM/GBSA or related binding energy estimates
• Tools: GROMACS, AMBER, CHARMM
Deliverables
MD stability reports
Interaction persistence heatmaps
Binding energy rankings
Clear “go/no-go” guidance
4. SAR Interpretation & Medicinal Chemistry Support
Scope
• Structural explanation of SAR trends
• Hotspot and interaction analysis
• Matched molecular pair interpretation
• Synthetic feasibility awareness
Deliverables
Design hypotheses with structural rationale
Suggested modifications with risk/benefit notes
Synthetic accessibility insights
5. Homology Modeling (When Structures Are Missing)
Scope
• Template selection and alignment
• Model refinement and validation
• Docking-ready structure generation
Deliverables
Validated homology models
Confidence assessment and limitations
Docking grids and setup files
Engagement Packages with Indicative Pricing
| Package | Best for | Scope | Timeline | Deliverables |
| Screening Sprint | Fast prioritization of early hits to avoid wasted effort | Library curation, docking, top 100 ranked compounds with poses | 2–4 weeks | Docking report, pose files, interaction maps, next-step recommendations |
| ADMET Model Build | Automated ranking of large compound sets with ML | QSAR/ML models for 2–3 endpoints + validation | 4–6 weeks | Models, performance metrics, notebooks or API, applicability domain doc |
| Hit-to-Lead Support | Ongoing SAR-driven design support during optimization | SAR review, design proposals, MD for top candidates | Monthly | Design trays, MD reports, decision logs |
| Data Foundation | Clean, connected data for confident decisions | ETL pipelines, data warehouse, Power BI dashboards | 6–8 weeks | Clean datasets, dashboards, SOPs |
| Fractional Scientist | Flexible senior-level discovery expertise on demand | 10–40 hrs/week across services | Ongoing | Roadmap, weekly updates, integrated outputs |
| Pricing ranges are indicative and depend on target complexity, dataset size, endpoints, and delivery requirements. Final scope and pricing are confirmed after a discovery call. | ||||
Our integrated service platform leverages the power of AI-driven drug discovery to fundamentally transform the early-stage R&D pipeline. By combining advanced cheminformatics with deep learning architectures, we can rapidly navigate vast chemical spaces to identify high-affinity ligands and optimize lead compounds with surgical precision. This computational approach significantly de-risks the discovery process, predicting ADMET properties and structural stability before expensive wet-lab synthesis even begins. Our workflow bridges the gap between digital prediction and biological reality, utilizing iterative feedback loops to refine models based on real-world experimental data. Ultimately, we provide our partners with a streamlined path from initial target identification to validated preclinical candidates, accelerating the delivery of life-saving therapeutics.
Explore Our Recent Cheminformatics & Computational Drug Discovery Projects
Traditional Drug Discovery Vs Python-Powered Drug Discovery
The drug discovery process typically
begins with identifying a specific
molecular target associated with a disease
or condition. This could be a protein,
enzyme, receptor, or other biomolecule
involved in the disease pathway.
Validation of the target involves
confirming its relevance to the disease and
its potential as a therapeutic target
Once the target is validated, the next step
is to identify or generate “hits,” which are
compounds that have the potential to
interact with the target and modulate its
activity. Hits can be identified through
various methods, including high-
throughput screening of chemical libraries,
virtual screening using computational
methods, or fragment-based screening.
Selected hits are further optimized to
improve their potency, selectivity,
pharmacokinetic properties, and safety
profile. This process involves medicinal
chemistry techniques to modify the
chemical structure of the hits while
maintaining or enhancing their biological
activity. Iterative cycles of synthesis,
testing, and structure-activity relationship
(SAR) analysis are conducted to identify
lead compounds with improved drug-like
properties.
Lead compounds with the most promising
pharmacological profiles are subjected to
further optimization to enhance their
efficacy, safety, and drug-like properties.
This involves fine-tuning the chemical
structure of the lead compounds and
evaluating their pharmacokinetic and
toxicological properties through in vitro
and in vivo studies. The goal is to identify
candidate compounds suitable for
preclinical testing.
Candidate compounds undergo preclinical
testing to assess their safety,
pharmacokinetics, pharmacodynamics, and
toxicology in animal models. These studies
provide crucial data for evaluating the
compound’s potential for human use and
determining the optimal dose range for
clinical trials.
Phase I: Conducted in a small number of healthy volunteers to evaluate the
compound’s safety, pharmacokinetics, and
initial tolerability.
Phase II: Involves testing the compound
in a larger group of patients to assess its
efficacy and further evaluate safety.
Phase III: Conducted in a larger patient
population to confirm efficacy, monitor
adverse effects, and gather additional
safety data. Successful completion of
Phase III trials may lead to regulatory
approval for marketing
Utilize Python libraries like Biopython for
sequence analysis and protein structure
prediction to identify potential drug
targets.
Analyze omics data using Pandas,
NumPy, and SciPy to identify genes,
proteins, or pathways associated with
diseases
Use Python libraries like RDKit for virtual
screening, molecular docking, and
ligand-based methods to identify
chemical compounds with potential
activity against the target.
Implement QSAR modeling using Scikit-
learn or TensorFlow to predict the
activity of compounds based on their
chemical structure.
Apply molecular dynamics simulations
and free energy calculations using tools
like MDAnalysis and OpenMM to
optimize lead compounds for potency
and selectivity.
Perform structure-activity relationship
(SAR) analysis using Python to guide
chemical modifications and improve
compound potency.
Python libraries like RDKit, Autodock Vina,
and PyRx can be utilized for molecular
docking studies to predict the binding
affinity and binding modes of small
molecules with target proteins.
8. Python libraries such as Scikit-learn, Pandas,
and RDKit are commonly used for data
preprocessing, feature selection, model
building, and evaluation in QSAR studies.
9. Python frameworks like Scikit-learn,
TensorFlow, and PyTorch enable the
development of machine learning models.
By leveraging Python in pre-clinical
development for drug discovery, researchers
can streamline processes, analyze data more
effectively, and make informed decisions in
advancing potential drug candidates.
Python libraries like RDKit, OpenBabel, and
DeepChem can be used (ADME-Tox)
properties of compounds through machine
learning models.
Phase I: Conducted in a small number ofhealthy volunteers to evaluate the
compound’s safety, pharmacokinetics,
and initial tolerability.
Phase II: Involves testing the compound
in a larger group of patients to assess its
efficacy and further evaluate safety.
Phase III: Conducted in a larger patient
population to confirm efficacy, monitor
adverse effects, and gather additional
safety data. Successful completion of
Phase III trials may lead to regulatory
approval for marketing
Traditional drug discovery research has been successfully conducted for pharmaceutical companies including Pharmacopeia, Purdue Pharma, Apotex Inc., and Pfizer.

Benzenesulfonamide compounds and their use

4-(2-Pyridyl)piperazine-1-carboxamides: Potent vanilloid receptor 1 antagonists

Solidphase synthesis of isoindolines via a rhodium-catalyzed [2+2+2] cycloaddition
https://www.researchgate.net/scientific-contributions/Khondaker-Islam-34615828

1,3-Dihydro-2,1,3-benzothiadiazol-2,2-diones and 3,4-dihydro-1H-2,1,3- benzothidiazin-2,2-diones as ligands for the NOP receptor
https://www.researchgate.net/scientific-contributions/Khondaker-Islam-34615828