SwissBioisostere 2021: updated structural, bioactivity and physicochemical data delivered by a reshaped web interface.

At several stages of drug discovery, bioisosteric replacement is a common and efficient practice to find new bioactive chemotypes or to optimize series of molecules toward drug candidates. The critical steps consisting in selecting which molecular moiety should be replaced by which other chemical fragment is often relying on the expertise of specialists. Nowadays, valuable support can be obtained through the wealth of dedicated structural and knowledge data. The present article details the update of SwissBioisostere, a database of >25 millions of unique molecular replacements with data on bioactivity, physicochemistry, chemical and biological contexts extracted from the literature and related resources. The content of the database together with analysis and visualization capacities is freely available at www.swissbioisostere.ch.

Computational fluid dynamics analysis of mixing and gas-liquid mass transfer in wave bag bioreactor.

Single use bioreactors provide an attractive alternative to traditional deep-tank stainless steel bioreactors in process development and more recently manufacturing process. Wave bag bioreactors, in particular, have shown potential applications for cultivation of shear sensitive human and animal cells. However, the lack of knowledge about the complex fluid flow environment prevailing in wave bag bioreactors has so far hampered the development of a scientific rationale for their scale up. In this study, we use computational fluid dynamics (CFD) to investigate the details of the flow field in a 20-L wave bag bioreactor as a function of rocking angle and rocking speed. The results are presented in terms of local and mean velocities, mixing, and energy dissipation rates, which are used to create a process engineering framework for the scale-up of wave bag bioreactors. Proof-of-concept analysis of mixing and fluid flow in the 20-L wave bag bioreactor demonstrates the applicability of the CFD methodology and the temporal and spatial energy dissipation rates integrated and averaged over the liquid volume in the bag provide the means to correlate experimental volumetric oxygen transfer rates (kL a) data with power per unit volume. This correlation could be used as a rule of thumb for scaling up and down the wave bag bioreactors.

Developing a Computational Framework To Advance Bioprocess Scale-Up.

Bioprocess scale-up is a critical step in process development. However, loss of production performance upon scaling-up, including reduced titer, yield, or productivity, has often been observed, hindering the commercialization of biotech innovations. Recent developments in scale-down studies assisted by computational fluid dynamics (CFD) and powerful stimulus-response metabolic models afford better process prediction and evaluation, enabling faster scale-up with minimal losses. In the future, an ideal bioprocess design would be guided by an in silico model that integrates cellular physiology (spatiotemporal multiscale cellular models) and fluid dynamics (CFD models). Nonetheless, there are challenges associated with both establishing predictive metabolic models and CFD coupling. By highlighting these and providing possible solutions here, we aim to advance the development of a computational framework to accelerate bioprocess scale-up.

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level.

Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds are incrementally converted into simpler compounds until the starting compounds are commercially available. However, predictions based on small chemical datasets often result in low accuracy due to an insufficient number of samples. To address this limitation, we introduced transfer learning to retrosynthetic analysis. Transfer learning is a machine learning approach that trains a model on one task and then applies the model to a related but different task; this approach can be used to solve the limitation of few data. The unclassified USPTO-380K large dataset was first applied to models for pretraining so that they gain a basic theoretical knowledge of chemistry, such as the chirality of compounds, reaction types and the SMILES form of chemical structure of compounds. The USPTO-380K and the USPTO-50K (which was also used by Liu et al.) were originally derived from Lowe's patent mining work. Liu et al. further processed these data and divided the reaction examples into 10 categories, but we did not. Subsequently, the acquired skills were transferred to be used on the classified USPTO-50K small dataset for continuous training and retrosynthetic reaction tests, and the pretrained accuracy data were simultaneously compared with the accuracy of results from models without pretraining. The transfer learning concept was combined with the sequence-to-sequence (seq2seq) or Transformer model for prediction and verification. The seq2seq and Transformer models, both of which are based on an encoder-decoder architecture, were originally constructed for language translation missions. The two algorithms translate SMILES form of structures of reactants to SMILES form of products, also taking into account other relevant chemical information (chirality, reaction types and conditions). The results demonstrated that the accuracy of the retrosynthetic analysis by the seq2seq and Transformer models after pretraining was significantly improved. The top-1 accuracy (which is the accuracy rate of the first prediction matching the actual result) of the Transformer-transfer-learning model increased from 52.4% to 60.7% with greatly improved prediction power. The model's top-20 prediction accuracy (which is the accuracy rate of the top 20 categories containing actual results) was 88.9%, which represents fairly good prediction in retrosynthetic analysis. In summary, this study proves that transferring learning between models working with different chemical datasets is feasible. The introduction of transfer learning to a model significantly improved prediction accuracy and, especially, assisted in small dataset based reaction prediction and retrosynthetic analysis.

PETRA: Drug Engineering via Rigidity Analysis.

Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of new medicines with high efficacies. Nonetheless, drug development remains a time and cost intensive process. In this work, we have developed a computational pipeline for assessing how individual atoms contribute to a ligand's effect on the structural stability of a biological target. Our approach takes as input a protein-ligand resolved PDB structure file and systematically generates all possible ligand variants. We assess how the atomic-level edits to the ligand alter the drug's effect via a graph theoretic rigidity analysis approach. We demonstrate, via four case studies of common drugs, the utility of our pipeline and corroborate our analyses with known biophysical properties of the medicines, as reported in the literature.

Women Make COMP: Mentoring the Next Generation of Women in Computational Chemistry.

The Women Make COMP symposium (258th American Chemical Society Meeting) aims at inspiring, motivating, and supporting young women in computational and theoretical chemistry. As a role model of the event, Ada Lovelace (1815-1852) was an English mathematician and writer, known for having founded computing science.