nach0: multimodal natural and chemical languages foundation model.

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.

Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering.

The SARS-CoV-2 virus like many other viruses has transformed in a continual manner to give rise to new variants by means of mutations commonly through substitutions and indels. These mutations in some cases can give the virus a survival advantage making the mutants dangerous. In general, laboratory investigation must be carried to determine whether the new variants have any characteristics that can make them more lethal and contagious. Therefore, complex and time-consuming analyses are required in order to delve deeper into the exact impact of a particular mutation. The time required for these analyses makes it difficult to understand the variants of concern and thereby limiting the preventive action that can be taken against them spreading rapidly. In this analysis, we have deployed a statistical technique Shannon Entropy, to identify positions in the spike protein of SARS Cov-2 viral sequence which are most susceptible to mutations. Subsequently, we also use machine learning based clustering techniques to cluster known dangerous mutations based on similarities in properties. This work utilizes embeddings generated using language modeling, the ProtBERT model, to identify mutations of a similar nature and to pick out regions of interest based on proneness to change. Our entropy-based analysis successfully predicted the fifteen hotspot regions, among which we were able to validate ten known variants of interest, in six hotspot regions. As the situation of SARS-COV-2 virus rapidly evolves we believe that the remaining nine mutational hotspots may contain variants that can emerge in the future. We believe that this may be promising in helping the research community to devise therapeutics based on probable new mutation zones in the viral sequence and resemblance in properties of various mutations.

Efficacy of spent tea waste as chemically impregnated adsorbent involving ortho-phosphoric and sulphuric acid for abatement of aqueous phenol-isotherm, kinetics and artificial neural network modelling.

The current study emphasises on sorptive expulsion of phenol from aqueous solution using ortho-phosphoric acid (STAC-O) and sulphuric acid (STAC-H)-activated biochar derived from spent tea waste. STAC-O and STAC-H were instrumentally anatomised using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), BET surface area and thermal gravimetric analyser. Equilibrium and kinetic data were implemented for the investigative parametric batch study to prospect the influence of adsorbent dosage, contact time, initial concentration and pH for eradication of phenol from aqueous solution. The maximum phenolic removals by STAC-O and STAC-H are 93.59% and 91.024% respectively at the parametric conditions of adsorbent dosage 3 g/l time 2 h, initial phenol concentration 100 mg/l and pH 8. Non-linear regression of adsorption isotherms and kinetics was accomplished using the equilibrium data. Both the specimens were compared, and it delineated that Temkin isotherm model is contented. The maximum adsorption intakes for STAC-H and STAC-O were 185.002 mg/g and 154.39 mg/g respectively. Pseudo-second-order kinetic model was best fitted for portraying the chemisorption phenomena. Boyd kinetic and intra-particle diffusion model were investigated to elucidate the diffusion mechanism involved in the process. Desorption study was employed for determining the regeneration proficiency of the adsorbents using water, ethanol and NaOH with maximum 93% and 51.16% extrusion for STAC-O and STAC-H respectively. The process parameters involved in this study were further analysed using artificial neural network perusal to determine the input-output relationships and data pattern. The overall adsorption study along with cost estimation exhibited that bidirectional activation of spent tea biochar was prospective in abatement of phenol from aqueous media.