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About

Dmitry A. Shulga

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Ph.D. in chemistry (molecular modeling), MBA

Expertise and Services

Best of our experite to serve your projects.

Molecular modeling core expertise

  • Cheminformatics
  • QSAR/QSPR
  • Machine learning
  • Anisotropic electrostatics
  • Halogen bonding
  • Force field development
  • Conformational analysis
  • Molecular dynamics
  • Virtual screening/docking
  • Quantum chemistry

IT related expertise

  • Scientific sowtware development
  • Algorithm development
  • Numerical methods
  • Scripting processing pipelines
  • Robust optimization
  • Big Data analysis / Hadoop
  • Parallel computing
  • Cloud computing
  • Docker abstraction
  • Databases, SQL, BigQuery

Scientific metrics

Ph.D. in chemistry

25+ scientific papers

Scholar.google

12+ scientific grants - in 5 as leader

At Lomonosov Moscow State University

supervision of:

  • 5 MS diploma
  • 4 graduate students

Med.Chem.Lab's personal page

IT best practices

  • Requirements gathering/negotiation
  • Agile/Scrum
  • Waterfall
  • MVP definition and inplementation
  • Crossfunctional teams
  • Test driven design
  • Code review

How we work

We value win/win approach to deliver best possible solution to address real customer needs within reasonable budget and time constraints.

Appreciate Agile

A means to accomplish the most meaningful work

  • Value MVP
  • Goals and means refinements as necessary
  • Incremental work using backlog

Startup thinking

Understanding startup mindset/reality

A.Osterwalder's canvas

Regular business

Processes, projects, hierarchy, leadership, functions

Six sigma

MBA degree

Experience

We believe our diverse experience helps to streamline the paths to successful project completion.

Short stats

  • 20+ years in IT and Drug discovery
  • 7+ years in business and B2B sales
  • 5+ years in startup environment

Contract RnD

  • Drug discovery
  • Polymer science
  • Adhesive research
  • Diffusion modeling
  • Dyes development

Innovations management

  • Assessment of corporate culture (AS IS)
  • Steps to enforce innovations (TO BE)

Viable network

  • Drug discovery researchers
  • Machine learners
  • Medicinal chemists
  • Senior management and owners

Customers and Partners