01
6+ Top-Tier Publications
Published in Nature Computational Materials, JACS, ACS Cent. Sci., and Nano Letters as first or equal-contribution author.
Open to AI/ML Scientist Roles · Drug Discovery
Hi, I'm Sourav.
AI/ML Scientist · Computational Chemist · Drug Discovery
Computational scientist with 10+ years bridging quantum chemistry and deep learning to accelerate drug discovery. Currently at Aganitha Cognitive Solutions — building end-to-end pipelines for crystal structure prediction, conformer generation, pKa estimation, and ADMET property calculations. Published in Nature Computational Materials, JACS, and Nano Letters.
Milestones
Key
Key
Achievements
A proven track record of shipping production pipelines, autonomous agents, and highly-cited research in computational drug discovery.
02
6+ Production Pipelines in Igniva™
Built CrystalCleanPro, CrystalLatticeAI, MolConSUL Pro, SitepKa, QM-pKa, and ADMET platforms — shipped as part of Aganitha's commercial Agentic AI product.
03
AI Agent for Lead Optimization
Autonomous medicinal chemistry agent using Claude AI with iterative propose-score-compare loops for drug lead optimization.
04
End-to-End Drug Discovery Platform
Full-stack triage app (React, FastAPI, RDKit) with real-time ADMET, QED, and PAINS screening deployed on Vercel.
Computational Stack
AI/ML for Drug Discovery
- Graph Neural Networks
- PyTorch / TensorFlow
- Transformers
- ADMET Prediction
- AI Agents
Computational Chemistry
- CP2K / ORCA / PySCF
- DFT / SAPT
- ML Force Fields
- Molecular Docking
- pKa Prediction
Cheminformatics
- RDKit / ASE
- SMILES / SMARTS
- Conformer Generation
- Crystal Structure Pred.
- ADMET Workflows
MLOps & Languages
- Python (Expert)
- Docker / Azure
- CI/CD / MLflow
- NumPy / Pandas
- Fortran / Bash
Projects & Research
Scientist · Aganitha Cognitive Solutions · Jan 2024 – Present
Production AI/ML Pipelines for Pharmaceutical R&D
PyTorch
GNNs
RDKit
CP2K
Docker
Azure
Crystal Structure Prediction
GNN pipeline for rapid polymorph screening of drug-like molecules — predicting stable crystal forms critical to formulation.
Conformer Generation
Environment-aware AI platform generating realistic 3D conformers accounting for solvent and protein-pocket context.
pKa Estimation
Dual-model approach — QM-based and GNN-based — for accurate pKa prediction under non-aqueous conditions.
ADMET Prediction
Physics-based QM platform predicting solubility, logP, and solvation energy for high-accuracy ADMET profiling.
Featured
Full-Stack · Live Demo
Drug Discovery Triage Platform
Full-stack drug discovery triage application with real-time ADMET property predictions, QED scoring, PAINS alerts, and interactive 2D molecular visualization for rapid compound screening.
- Real-time ADMET: lipophilicity, solubility, permeability
- QED drug-likeness scoring & PAINS substructure alerts
- Dockerized deployment with CI/CD pipeline
GNN · R²=0.90
Graph Neural Networks
Solubility Dual-Graph GNN
Dual-graph interaction GNN predicting molecular solubility (logS) from solute-solvent SMILES pairs. Bidirectional cross-attention mechanism trained on 100K+ BigSolDB 2.0 pairs achieves R² = 0.90, RMSE = 0.388.
- Separate MPNN encoders for solute & solvent graphs
- Bidirectional cross-attention for interaction modeling
- 100,983 solute-solvent pairs from BigSolDB 2.0
AI Agents
Claude AI · HuggingFace
Lead Optimization Agent
AI-powered medicinal chemistry sandbox using Claude to iteratively propose and score structural modifications. Autonomous loop: propose → score → compare → iterate with full property trajectory tracking.
- BBB, CNS MPO, QED, and flexibility tracked per attempt
- Highlighted 2D structures showing structural changes
- Local run persistence — resume without extra LLM cost
GNN · RL · MLflow
Binding Affinity · REINFORCE
GNN Bind Optimizer
Heterogeneous GNN for protein-ligand binding affinity prediction paired with a REINFORCE-based molecular generator. End-to-end pipeline with SQL Server persistence, MLflow experiment tracking, and an interactive Streamlit UI for pocket-aware ligand design.
- HGTConv heterogeneous GNN — MTL & STL training modes
- REINFORCE RL loop for pocket-conditioned molecule generation
- Docker Compose full-stack: SQL Server + MLflow + Streamlit
Bayesian Opt · GP
Tablet Formulation · BoTorch
Formulation Bayesian Optimization
Model-based DoE pipeline replacing OFAT/grid screening with uncertainty-aware sequential experimentation for pharmaceutical tablet formulation. GP surrogate models maximize Q45 (% drug dissolved at 45 min) under hard mass-balance constraints across a 5-excipient design space (HPMC, MCC, CCS, MgSt, PVP K30).
- Single-objective BO: GP + EI/LogEI to maximize Q45 with fewer wet-lab runs
- Multi-objective BO: qNEHVI Pareto front over Q45, hardness & friability
- Streamlit dashboard: design-space exploration, experiment logging & convergence comparison
Recent News
May 2026
New Project
Launched: Formulation Bayesian Optimization
Released model-based DoE pipeline for pharmaceutical tablet formulation. GP surrogate + EI/qNEHVI acquisition maximizes dissolution (Q45) and multi-objective trade-offs under hard excipient constraints — replacing costly OFAT screening.
2025
Publication
Paper published in ACS Central Science
"The spin phonon relaxation of single molecules magnet in the presence of strong exchange coupling" — first-principles study linking phonon coupling to spin relaxation rates in molecular magnets.
Jan 2024
Role
Joined Aganitha Cognitive Solutions as Scientist
Building production AI/ML pipelines for pharmaceutical R&D — crystal structure prediction, conformer generation, pKa estimation, and ADMET profiling within Aganitha's commercial Igniva™ platform.
2023
Publication
Paper published in npj Computational Materials
"Spin-phonon decoherence in solid-state paramagnetic defects from first principles" — ab-initio framework for computing spin coherence times in qubit-relevant defect systems.
Selected Publications
Published in Nature Computational Materials · JACS · Nano Letters · J. Phys. Chem. Lett.
2025
ACS Cent. Sci.
The spin phonon relaxation of single molecules magnet in the presence of strong exchange coupling
Sourav Mondal, Julia Netz et al.
2023
npj Comput. Mat.
Spin-phonon decoherence in solid-state paramagnetic defects from first principles
Sourav Mondal and A. Lunghi
2022
JACS
Unravelling the contributions to spin-lattice relaxation in Kramers single-molecule magnets
Sourav Mondal and A. Lunghi — J. Am. Chem. Soc., 50, 22965
2022
J. Phys. Chem. Lett.
Identification and Manipulation of Defects in Black Phosphorus
R. Harsh, Sourav Mondal*, D. Sharma et al. — 13, 6276 *Equal contribution
2020
Nano Letters
Direct Observation of the Reduction of a Molecule on a Nitrogen pair in Doped Graphene
M. Bouatou, Sourav Mondal*, C. Chacon et al. — 20, 6908 *Equal contribution
2019
J. Phys. Chem. Lett.
Importance of Epitaxial Strain at a Spin-Crossover Molecule Metal Interface
C. Fourmental, Sourav Mondal*, R. Banerjee et al. — 10, 4103 *Equal contribution
Experience & Education
Current
Jan 2024 – Present
Scientist
Aganitha Cognitive Solutions · Hyderabad
- Developing CSP pipelines with Graph Neural Networks for drug polymorph prediction
- Building AI models for conformer generation, pKa prediction, and ADMET properties
- Creating thermodynamic workflows: solubility, logP, and solvation energy
- Managing projects, client communications, and proposal writing
Previous
Aug 2023 – Dec 2023
Computational Scientist & Deep Learning Engineer
QpiVolta Technologies · Bangalore
- Developed machine learning force fields for solid-state electrolytes
- Modeled thermodynamic properties using molecular dynamics
- Led contract research on complex biological systems simulation
Postdoc
Mar 2021 – Jul 2023
Postdoctoral Researcher
Trinity College Dublin · Ireland
- Built neural network models for quantum property prediction
- Developed computational methods for spin dynamics simulation
- Published in Nature Computational Materials and JACS
- Combined ab-initio methods with machine learning approaches
PhD
Jan 2015 – Feb 2021
PhD in Computational Material Science
JNCASR · Bangalore
- Thesis: Tailoring Properties of 2D Systems via Molecular Adsorption and Defect Engineering
- DFT study of electronic and magnetic properties of 2D materials
- Published in Nano Letters and J. Phys. Chem. Letters
- Received CEFIPRA grant for research visits to Paris
M.Sc
2012 – 2014
M.Sc in Chemistry
IIT Guwahati
Qualified NET CSIR-UGC Junior/Senior Research Fellowship