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Gradient Bulgaria provides bio-informatincs services connected with quantum representation of bio-molecular interactions. The company has access to unique proprietary technology for quantum similarity-based molecular modelling, developed by Gradient Biomodeling LLC.

This technology has been successfully applied and experimentally validated against a variety of therapeutic targets, discovering the following structurally novel compounds: Non-toxic and highly active compounds against blood-stage malaria (PMID: 21933377).; non-toxic, bioavailable potentiators of Nrf2 pathway that penetrate the blood-brain-barrier (PMID: 22925725).; non-toxic, bioavailable compounds active against liver-stage malaria (PMID: 25951139). One of these compounds, cethromycin, is being investigated for drug repositioning and its novel use has been patented. Gradient’s technology has discovered also drug repositioning candidates against human African trypanosomiasis.

Gradient Bulgaria is responsible for the creation and maintenance of specific databases necessary for the quantum molecular modeling methodology, as well as for data integration and annotation of results. The company maintains bio- and chem-informatics procedures related with quantum similarity–based molecular modeling. Gradient's team has managed various in-silico- driven R&D projects both in Europe and in the United States.

About Gradient

Tsvetan Mladenov

Tsvetan has more than 15 years of executive experience including as IT Systems Leader of PricewaterhouseCoopers for Central and Eastern Europe. In the last 5 years Mr. Mladenov has been instrumental in building the bioinformatics team capable of addressing Gradient’s specific bio- and chem-informatics needs.

Iva Krasteva, PhD

Iva is responsible for computational quantum molecular-specific scientific database creation and maintenance, as well as computational performance and efficiency. She obtained a PhD in Computer Science from Sofia University in 2011. Iva has more than 10 years of experience in software development projects andhas been actively involved in various national and European RTD projects.

Lyubomir Nashev, PhD

Lyubomir manages scientific operations at Gradient since 2012. He holds s PhD in Biochemistry and Molecular medicine from University of Berne and he has made a four year post-doctoral training at Pharmacetrum at the University of Basle, Switzerland. He has also worked as bioinfrormatician in Metalife AG, Bulgaria.Lyubomir is in charge of cheminformatic pre- and post-processing of molecular databases and related pharmacological models.

Technology

In principle, all bio-molecular interactions are quantum in nature and do not depend explicitly on chemical structure. Thus, structurally dissimilar bio-active compounds can be similar on a quantum level, enabling our procedure to identify novel structural classes with similar biological activity. The modelling platform used by Gradient Bulgaria leverages non-relativistic quantum mechanics to deliver an adequate level of physical theory for mathematical modelling of molecular and biomolecular systems. The methodology is based on molecular quantum representations and adaptive machine-learning, fuzzy decision-making algorithms. More detailed description can be found at: http://www.gradientbiomodeling.com/technologies

Two chemically dissimilar anti-malarials (dihydroartemisinin and fosmidomycin) share a very high degree of relevant quantum similarity

The quantum components (QCs) on which Gradient’s technology is based are rigorous well-defined, easily computable, localized molecular attributes. QCs are derived from a special representation of quantum fields, and their well-defined mathematical characteristics afford systematic theoretical treatment and property prediction on a scale computationally infeasible with more traditional methods.

Our molecular screening database contains natural and synthetic compounds, as well as completely novel easy-to-synthesize compounds, all prepared in proprietary molecular format to be used in Gradient's quantum models.

Projects

High throughput quantum molecular modeling of co-crystallographic data

The project includes development of platform for automatic molecular modeling of co-crystallographic data of proteins in complex with small molecules. The quantum-similarity-based molecular modeling of crystallographic is more rigorous and reliable than traditional ligand docking algorithms since it does not depend on spatial orientation of candidate molecules. Potential applications vary from lead pharmaceuticals discovery to discovery of molecular probes for biomedical research applications.

Systematic drug repurposing

A platform for data-driven systematic drug repurposing has been build that aims at discovery of novel repurposing candidates against both rare and neglected diseases and also metabolic and neurodegenerative diseases. This is achieved by utilization of multi-step quantum-similarity procedure for identification of novel biological activities of approved pharmaceuticals, developmental drugs, and compounds with human safety data. So far the utilized methodology has led to drug repurposing candidates against liver stage malaria and sleeping sickness.

Contacts