4 Gromacs Jobs

Join us and contribute to the discovery of medicines that will impact lives! Hyderabad, India | Hybrid | Full-Time About Aganitha High-throughput experimentation, computational & data-driven modeling, and the advent of the Open Science era are fundamentally transforming research, discovery, and development across diverse industries. Aganitha is at the forefront of co-innovation with global clients, shaping next-generation R&D (aganitha.ai). While our primary client base has been in global Biopharma, we are actively expanding our collaborations into consumer brands and, in the near future, the materials design industry. As a Scientist at Aganitha, you will be instrumental in leveraging cutting-edge advances in computational chemistry, materials science, soft matter physics, AI/ML, and high-performance computing in the Cloud. You will contribute to accelerating design and development across a spectrum of applications, including but not limited to: small molecule therapeutics, biologics, gene, cell & RNA therapies within Biopharma; new product formulations for consumer brands; and novel materials for various industrial applications. You will collaborate closely with research leaders at our client organizations, identifying their needs and designing innovative solutions. Working with our internal technical and scientific teams, you will drive solutions from concept to launch and growth. You may also interact with external vendors to coordinate experimental validation of the in silico solutions developed at Aganitha. To excel in this role, you must possess a strong interest in engaging with customers to apply the latest scientific and technological advancements for R&D acceleration, thereby contributing to Aganitha’s growth. Key Responsibilities Design and develop AI/ML models to solve complex problems in computational chemistry, such as predicting molecular properties, material behaviors, or reaction outcomes. Curate, process, and analyze scientific datasets from various sources, including literature and experimental data, ensuring data quality and readiness for model training. Develop intelligent featurization strategies that accurately represent chemical structures, physical properties, or biological interactions, drawing upon your scientific understanding. Implement, train, and evaluate cutting-edge Machine Learning and Deep Learning algorithms (e.g., CNNs, RNNs, LSTMs, Transformer architectures) to build robust predictive models. Rigorously validate models against client-provided data and established benchmarks, ensuring their accuracy, generalizability, and utility. Translate complex technical and scientific findings into clear, actionable insights for both technical and non-technical stakeholders. Collaborate effectively with computational chemists, data scientists, and wet lab scientists to define project requirements, iterate on solutions, and ensure successful deployment. Stay abreast of the latest advancements in AI/ML, computational chemistry, and relevant scientific domains, continuously seeking opportunities to apply new methodologies. Qualifications PhD, post-doctoral research, or equivalent higher studies in Computational Chemistry, Cheminformatics, Materials Science, Chemical Engineering, Biochemistry, or a closely related scientific discipline. Strong foundational understanding of the scientific principles underlying computational chemistry, materials science, or related fields (e.g., colloidal chemistry, polymer science, surfactant chemistry, soft condensed matter physics, etc.). Solid mathematical intuition of Machine Learning algorithms, including Deep Learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, and Transformer architectures. Proficiency in modular, typed, and object-oriented Python programming. A high-level understanding of the ML/DL project lifecycle , from data preparation and feature engineering through model development, training, evaluation, and deployment. Excellent problem-solving skills and the ability to apply critical thinking to complex scientific and technical challenges. Strong verbal and written communication skills , with the ability to effectively communicate technical concepts to diverse audiences. Desired Technical Skills/Expertise Hands-on experience with molecular dynamics software packages (e.g., GROMACS, NAMD, AMBER, CHARMM,OpenMM, etc.). Familiarity with quantum chemistry software (e.g., PySCF, ORCA, NWChem, Quantum Espresso, etc.). Proficiency in at least one scripting/programming language, preferably Python, for data analysis, automation, and workflow development. Understanding of biomolecular docking and virtual screening methodologies. Exposure to data visualization tools for scientific data. Added Advantages Experience in effectively building and deploying ML solutions using popular frameworks such as PyTorch, TensorFlow, Keras, or scikit-learn. Proficiency in shell scripting. Big Plus Exposure to large language models (LLMs) such as ChatGPT, Claude, or Gemini, and practical experience in utilizing such tools for day-to-day work (e.g., writing research reports, understanding new concepts, generating code).

Join us and contribute to the discovery of medicines that will impact lives! Hyderabad, India | Hybrid | Full-Time About Aganitha High-throughput experimentation, computational & data-driven modeling, and the advent of the Open Science era are fundamentally transforming research, discovery, and development across diverse industries. Aganitha is at the forefront of co-innovation with global clients, shaping next-generation R&D (aganitha.ai). While our primary client base has been in global Biopharma, we are actively expanding our collaborations into consumer brands and, in the near future, the materials design industry. As a Scientist at Aganitha, you will be instrumental in leveraging cutting-edge advances in computational chemistry, materials science, soft matter physics, AI/ML, and high-performance computing in the Cloud. You will contribute to accelerating design and development across a spectrum of applications, including but not limited to: small molecule therapeutics, biologics, gene, cell & RNA therapies within Biopharma; new product formulations for consumer brands; and novel materials for various industrial applications. You will collaborate closely with research leaders at our client organizations, identifying their needs and designing innovative solutions. Working with our internal technical and scientific teams, you will drive solutions from concept to launch and growth. You may also interact with external vendors to coordinate experimental validation of the in silico solutions developed at Aganitha. To excel in this role, you must possess a strong interest in engaging with customers to apply the latest scientific and technological advancements for R&D acceleration, thereby contributing to Aganitha’s growth. Key Responsibilities Perform advanced computational simulations (e.g., Periodic and non-periodic Quantum Mechanics, Atomistic and/or Coarse-grained variants of Molecular Dynamics, Monte Carlo, Brownian Dynamics, Langevin Dynamics, Dissipative Particle Dynamics, etc.) to understand stability, complex interactions and design principles relevant to various hard and soft matter systems e.g., inorganic/organic crystalline materials, surfactants, polymers, colloids, biomolecules, etc. Apply computational methods to materials design for various applications such as semiconductor devices, capture and storage of greenhouse gases, skin and body care formulations, excipients, etc. Conduct molecular modeling studies to investigate self-assembly phenomena and interactions between various components in the formulation of a material, such as surfactant or polymer interactions with diverse substrates (e.g., skin, hair, fabric). Interpret results from domain-specific simulations (e.g., MD, DFT) and structure the scientific data into features or descriptors—such as radial distribution functions, binding energies, surface areas, or density profiles—relevant for downstream AI/ML modeling of material properties or formulation performance. Understand, analyze, critique, and implement research papers, tailoring approaches to specific problem contexts. Develop clear and concise narratives of data-driven analyses performed using computational techniques. Effectively articulate and communicate complex domain knowledge to cross-functional teams. Participate actively in requirements gathering, design discussions, and demonstrations. Continuously learn and stay up-to-date on emerging technologies and scientific advancements in computational chemistry, materials science, and related fields—research opportunities for applying advanced computational methods to evolving industry challenges. Educational & Research Qualifications PhD, post-doctoral research, or equivalent higher studies in Computational Chemistry or Computational Physics applied to Materials Science, Polymer Science, Surfactant Science, Colloidal Chemistry, Soft Matter Physics, Fluid Dynamics, or a closely related field. Demonstrated first-hand research experience in problems in the domain of materials science, for example: Materials design for various applications such as semi-conductor devices, capture and storage of green-house gases, skin and body care formulations, excipients. Crystal structure prediction of inorganic/organic molecules Molecular/colloidal self-assembly and crystallization phenomena Interactions between various components in the formulation of a material, e.g., surfactant or polymer interactions with diverse substrates (e.g., skin, hair, fabric). Proficiency in at least one of the advanced computational chemistry methods viz. periodic and non-periodic Quantum Mechanics, Atomistic and/or Coarse-grained variants of Molecular Dynamics, Monte Carlo, Brownian Dynamics, Langevin Dynamics, Dissipative Particle Dynamics. Technical Skills Familiarity with computational chemistry packages such as Quantum Espresso, VASP, LAMMPS, PySCF, GROMACS, NAMD, CP2K, SIESTA, OpenMM, or similar. Must have a keen understanding of the interface of computer technology, high-throughput sciences, and chemistry/materials science. Added Advantages Familiarity with AI/ML methods and their application in scientific research. Expertise in computer programming (e.g., Python, C++, Fortran). Exposure to High-Performance Computing (HPC) environments and parallel computing. Soft Skills Excellent verbal and written communication skills are essential. Excellent communication skills, with the ability to distill complex scientific concepts into easily understandable insights for diverse audiences. Right attitude to collaborate effectively within a cross-functional team environment. Ability to quickly grasp new scientific domains and apply critical thinking to novel challenges. Comfortable working in a fast-paced, rapidly changing environment. Strong interest and aptitude to break down large, complex problem statements into manageable work packets.

Overview: Molecule AI, an innovative AI-based drug discovery company, seeks a Computational Scientist skilled in molecular modelling, molecular dynamics (MD) simulations, and machine learning for drug discovery. This role involves developing and evaluating computational methods for molecular property prediction, utilising molecular modelling for drug discovery, and working with AI models for de novo drug design. Relevant areas of experience include the direct application of MD to drug discovery, computer-aided drug design, molecular modelling and medicinal chemistry. Specific knowledge of any of these areas is less critical, and higher weightage will be placed on solid research skills and a history of innovation and accomplishment. Qualifications: Ph.D./M.Sc. from a reputed institution or university in a related field with a focus on drug discovery. Post-doctoral experience is desirable but not mandatory. Expertise in molecular modelling, MD simulations, and free energy calculations. Proficient in Python. Strong problem-solving and communication skills. Key Responsibilities: Computational Chemist in Drug Discovery 1. Molecular Modeling & Drug Design Conduct molecular docking (AutoDock, Unidock) to predict binding modes of small molecules. Perform virtual screening of compound libraries to identify potential hits. Use molecular dynamics (MD) simulations (GROMACS, AMBER, etc) to study protein-ligand interactions. Perform structure-based drug design (SBDD) and ligand-based drug design (LBDD). Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties using machine learning algorithms & AI-based approaches. 2. Cheminformatics & Data Analysis Analyze structure-activity relationships (SAR) to guide medicinal chemistry efforts. Apply machine learning (ML) & AI-based approaches for predictive modeling (QSAR, deep learning). Work with large chemical databases (ChEMBL, PubChem, ZINC) to extract relevant data. Develop pharmacophore models to identify key molecular features for activity. 3. Protein Modeling & Bioinformatics Perform homology modeling for proteins with unknown structures. Predict protein-ligand & protein-protein interactions. Analyze protein dynamics & conformational changes relevant to drug binding. 4. Collaboration with Experimental Teams Work closely with medicinal chemists to optimize lead compounds. Support biologists & pharmacologists in interpreting biochemical assay data. Validate computational predictions with experimental results (X-ray crystallography, NMR, biochemical assays). 5. Software & Tools Development Develop custom scripts (Python, & others) for data analysis & automation. Use quantum mechanics/molecular mechanics (QM/MM) for detailed electronic structure analysis. Stay updated with emerging computational tools (AlphaFold, DeepChem, DiffDock). 6. Documentation & Reporting Prepare technical reports & presentations for stakeholders. Maintain detailed records of computational experiments. Contribute to scientific publications and patent applications. The right candidate will have 2-5 years of industrial work experience and strong coding ability. **Interested candidates can send their CVs to info@moleculeai.com or priyanka.balodi@moleculeai.net to apply for this position.

Position Summary: We are seeking a highly motivated individual with expertise in computational chemistry to join our team as a Postdoctoral Fellow or Scientist, depending on experience level. The position will involve applying advanced computational methods to design, optimize, and analyze small molecules and their interactions with biological targets, contributing to drug discovery and disease modeling efforts. The successful candidate will have a PhD in computational chemistry, cheminformatics, physical chemistry, or a related field, with a deep understanding of molecular modeling, simulations, and structure-activity relationships. Fresh PhD graduates will be hired as Postdoctoral Fellows for a two-year term, while candidates with at least two years of postdoctoral fellowship or industry experience will be considered for a Scientist position. Key Responsibilities: Apply deep domain knowledge in computational chemistry to support drug discovery projects, including virtual screening, lead optimization, and ADME/Tox predictions. Utilize molecular modeling, molecular dynamics simulations, quantum mechanics, and docking techniques to understand ligand-protein interactions and predict binding affinities. Integrate chemical data with multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to identify novel therapeutic opportunities and understand disease mechanisms. Work with a team of computational biologists and software experts to develop and apply AI/ML approaches for chemical property prediction and drug design. Stay up to date with the latest advancements in computational chemistry, cheminformatics, and AI-driven drug discovery to incorporate cutting-edge research into project efforts. Collaboration & Project Management: Work closely with cross-functional teams, including medicinal chemists, biologists, computational scientists, and software engineers to enhance translational research efforts. Work with the Scientific Manager to help in project planning, execution, and reporting, ensuring alignment with research objectives. Communicate findings effectively through reports, presentations, and discussions with internal teams and external collaborators. Publish research findings in high-impact, peer-reviewed journals. Present work at scientific conferences, symposia, and internal research meetings. Contribute to grant applications and funding proposals where relevant. Qualifications & Experience: PhD in computational chemistry, cheminformatics, physical chemistry, or a closely related field. Strong expertise in molecular modeling, molecular dynamics simulations, quantum mechanics, and virtual screening techniques. Solid understanding of drug discovery principles, medicinal chemistry concepts, and structure-activity relationships. Proficiency with relevant computational chemistry software (e.g., Schrödinger, OpenEye, GROMACS, Amber) and programming languages (e.g., Python, R). Excellent verbal and written communication skills, with the ability to convey complex chemical and computational concepts clearly. Preferred: Experience with large-scale chemical data analysis and cheminformatics tools. Experience applying machine learning or artificial intelligence to chemical problems. Previous experience working in a multidisciplinary research environment, especially in a biopharmaceutical setting. Other Considerations: Fresh PhD graduates will be hired as Postdoctoral Fellows for a two-year term with an opportunity for promotion to Scientist based on performance. Candidates with at least two years of postdoctoral experience in computational chemistry in either academia or industry will be hired as Scientists with opportunities for career advancement. Why Join Us? Work at the cutting edge of computational chemistry and AI-driven drug discovery. Contribute to groundbreaking disease modeling and omics integration efforts. Collaborate with leading experts in multiple disease domains. Competitive salary, benefits, and career development opportunities. Graduation