An Effective Solution for Drug Discovery Based on the Tangram Meta-Heuristic and Compound Filtering

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2LGO- Pharmusing about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.Grant PID2021-123278OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”Projects PDC2022-133370-I00 and TED2021-132020B-I00 funded by MCIN/AEI/ 10.13039/5011 00011033 and by European Union Next GenerationEU/PRTRMinistry of Economic Transformation, Industry, Knowledge and Universities from the Andalusian government (PAIDI 2021: POSTDOC_21_00124)“Margarita Salas” grant (RR_A_2021_21), financed by the European Union (NextGenerationEU

Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Ligand Based Virtual Screening (LBVS) methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. This means, increasing accuracy of LBVS approaches may have a huge impact on increasing chances of success. Since the databases processed in drug discovery campaigns are enormously large, this pre-selection process requires the use of fast and precise methodologies. The similarity between compounds can be measured using different descriptors such as shape, pharmacophore or electrostatic similarity. The latter is the goal of this work, i.e., we want to improve the process of obtaining the compounds most similar to a query in terms of electrostatic similarity. To do so, the current and widely proposed methodology in the literature is based on the use of ROCS to assess the similarity of compounds in terms of shape and then evaluate a small subset of them with ZAP for prioritization regarding electrostatic similarity. This paper proposes an alternative methodology that consists of directly optimizing electrostatic similarity and works with the entire database of compounds without using shape cut-offs. For this purpose, a new and improved version of the OptiPharm software has been developed. OptiPharm implements a parameterizable metaheuristic algorithm able to solve any optimization problems directly related to the involved molecular conformations. We show that our new method completely outperforms the classical proposal widely used in the literature. Accordingly, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one. As a result, this methodology opens up new horizons in Drug Discovery.</div